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2019


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Attacking Optical Flow

Ranjan, A., Janai, J., Geiger, A., Black, M. J.

In International Conference on Computer Vision, November 2019 (inproceedings)

Abstract
Deep neural nets achieve state-of-the-art performance on the problem of optical flow estimation. Since optical flow is used in several safety-critical applications like self-driving cars, it is important to gain insights into the robustness of those techniques. Recently, it has been shown that adversarial attacks easily fool deep neural networks to misclassify objects. The robustness of optical flow networks to adversarial attacks, however, has not been studied so far. In this paper, we extend adversarial patch attacks to optical flow networks and show that such attacks can compromise their performance. We show that corrupting a small patch of less than 1% of the image size can significantly affect optical flow estimates. Our attacks lead to noisy flow estimates that extend significantly beyond the region of the attack, in many cases even completely erasing the motion of objects in the scene. While networks using an encoder-decoder architecture are very sensitive to these attacks, we found that networks using a spatial pyramid architecture are less affected. We analyse the success and failure of attacking both architectures by visualizing their feature maps and comparing them to classical optical flow techniques which are robust to these attacks. We also demonstrate that such attacks are practical by placing a printed pattern into real scenes.

Video Project Page Paper Supplementary Material link (url) [BibTex]

2019

Video Project Page Paper Supplementary Material link (url) [BibTex]


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Decoding subcategories of human bodies from both body- and face-responsive cortical regions

Foster, C., Zhao, M., Romero, J., Black, M. J., Mohler, B. J., Bartels, A., Bülthoff, I.

NeuroImage, 202(15):116085, November 2019 (article)

Abstract
Our visual system can easily categorize objects (e.g. faces vs. bodies) and further differentiate them into subcategories (e.g. male vs. female). This ability is particularly important for objects of social significance, such as human faces and bodies. While many studies have demonstrated category selectivity to faces and bodies in the brain, how subcategories of faces and bodies are represented remains unclear. Here, we investigated how the brain encodes two prominent subcategories shared by both faces and bodies, sex and weight, and whether neural responses to these subcategories rely on low-level visual, high-level visual or semantic similarity. We recorded brain activity with fMRI while participants viewed faces and bodies that varied in sex, weight, and image size. The results showed that the sex of bodies can be decoded from both body- and face-responsive brain areas, with the former exhibiting more consistent size-invariant decoding than the latter. Body weight could also be decoded in face-responsive areas and in distributed body-responsive areas, and this decoding was also invariant to image size. The weight of faces could be decoded from the fusiform body area (FBA), and weight could be decoded across face and body stimuli in the extrastriate body area (EBA) and a distributed body-responsive area. The sex of well-controlled faces (e.g. excluding hairstyles) could not be decoded from face- or body-responsive regions. These results demonstrate that both face- and body-responsive brain regions encode information that can distinguish the sex and weight of bodies. Moreover, the neural patterns corresponding to sex and weight were invariant to image size and could sometimes generalize across face and body stimuli, suggesting that such subcategorical information is encoded with a high-level visual or semantic code.

paper pdf DOI [BibTex]

paper pdf DOI [BibTex]


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AirCap – Aerial Outdoor Motion Capture

Ahmad, A., Price, E., Tallamraju, R., Saini, N., Lawless, G., Ludwig, R., Martinovic, I., Bülthoff, H. H., Black, M. J.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), Workshop on Aerial Swarms, November 2019 (misc)

Abstract
This paper presents an overview of the Grassroots project Aerial Outdoor Motion Capture (AirCap) running at the Max Planck Institute for Intelligent Systems. AirCap's goal is to achieve markerless, unconstrained, human motion capture (mocap) in unknown and unstructured outdoor environments. To that end, we have developed an autonomous flying motion capture system using a team of aerial vehicles (MAVs) with only on-board, monocular RGB cameras. We have conducted several real robot experiments involving up to 3 aerial vehicles autonomously tracking and following a person in several challenging scenarios using our approach of active cooperative perception developed in AirCap. Using the images captured by these robots during the experiments, we have demonstrated a successful offline body pose and shape estimation with sufficiently high accuracy. Overall, we have demonstrated the first fully autonomous flying motion capture system involving multiple robots for outdoor scenarios.

[BibTex]

[BibTex]


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Learning Multi-Human Optical Flow

Ranjan, A., Hoffmann, D. T., Tzionas, D., Tang, S., Romero, J., Black, M. J.

arxiv preprint arXiv:1910.1166, November 2019 (article)

Abstract
The optical flow of humans is well known to be useful for the analysis of human action. Recent optical flow methods focus on training deep networks to approach the problem. However, the training data used by them does not cover the domain of human motion. Therefore, we develop a dataset of multi-human optical flow and train optical flow networks on this dataset. We use a 3D model of the human body and motion capture data to synthesize realistic flow fields in both single-and multi-person images. We then train optical flow networks to estimate human flow fields from pairs of images. We demonstrate that our trained networks are more accurate than a wide range of top methods on held-out test data and that they can generalize well to real image sequences. The code, trained models and the dataset are available for research.

Paper poster link (url) [BibTex]


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Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop

Kolotouros, N., Pavlakos, G., Black, M. J., Daniilidis, K.

In International Conference on Computer Vision, October 2019 (inproceedings)

Abstract
Model-based human pose estimation is currently approached through two different paradigms. Optimization-based methods fit a parametric body model to 2D observations in an iterative manner, leading to accurate image-model alignments, but are often slow and sensitive to the initialization. In contrast, regression-based methods, that use a deep network to directly estimate the model parameters from pixels, tend to provide reasonable, but not pixel accurate, results while requiring huge amounts of supervision. In this work, instead of investigating which approach is better, our key insight is that the two paradigms can form a strong collaboration. A reasonable, directly regressed estimate from the network can initialize the iterative optimization making the fitting faster and more accurate. Similarly, a pixel accurate fit from iterative optimization can act as strong supervision for the network. This is the core of our proposed approach SPIN (SMPL oPtimization IN the loop). The deep network initializes an iterative optimization routine that fits the body model to 2D joints within the training loop, and the fitted estimate is subsequently used to supervise the network. Our approach is self-improving by nature, since better network estimates can lead the optimization to better solutions, while more accurate optimization fits provide better supervision for the network. We demonstrate the effectiveness of our approach in different settings, where 3D ground truth is scarce, or not available, and we consistently outperform the state-of-the-art model-based pose estimation approaches by significant margins.

pdf code project [BibTex]

pdf code project [BibTex]


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Resolving 3D Human Pose Ambiguities with 3D Scene Constraints

Hassan, M., Choutas, V., Tzionas, D., Black, M. J.

In International Conference on Computer Vision, October 2019 (inproceedings)

Abstract
To understand and analyze human behavior, we need to capture humans moving in, and interacting with, the world. Most existing methods perform 3D human pose estimation without explicitly considering the scene. We observe however that the world constrains the body and vice-versa. To motivate this, we show that current 3D human pose estimation methods produce results that are not consistent with the 3D scene. Our key contribution is to exploit static 3D scene structure to better estimate human pose from monocular images. The method enforces Proximal Relationships with Object eXclusion and is called PROX. To test this, we collect a new dataset composed of 12 different 3D scenes and RGB sequences of 20 subjects moving in and interacting with the scenes. We represent human pose using the 3D human body model SMPL-X and extend SMPLify-X to estimate body pose using scene constraints. We make use of the 3D scene information by formulating two main constraints. The interpenetration constraint penalizes intersection between the body model and the surrounding 3D scene. The contact constraint encourages specific parts of the body to be in contact with scene surfaces if they are close enough in distance and orientation. For quantitative evaluation we capture a separate dataset with 180 RGB frames in which the ground-truth body pose is estimated using a motion-capture system. We show quantitatively that introducing scene constraints significantly reduces 3D joint error and vertex error. Our code and data are available for research at https://prox.is.tue.mpg.de.

pdf poster link (url) [BibTex]


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End-to-end Learning for Graph Decomposition

Song, J., Andres, B., Black, M., Hilliges, O., Tang, S.

In International Conference on Computer Vision, October 2019 (inproceedings)

Abstract
Deep neural networks provide powerful tools for pattern recognition, while classical graph algorithms are widely used to solve combinatorial problems. In computer vision, many tasks combine elements of both pattern recognition and graph reasoning. In this paper, we study how to connect deep networks with graph decomposition into an end-to-end trainable framework. More specifically, the minimum cost multicut problem is first converted to an unconstrained binary cubic formulation where cycle consistency constraints are incorporated into the objective function. The new optimization problem can be viewed as a Conditional Random Field (CRF) in which the random variables are associated with the binary edge labels. Cycle constraints are introduced into the CRF as high-order potentials. A standard Convolutional Neural Network (CNN) provides the front-end features for the fully differentiable CRF. The parameters of both parts are optimized in an end-to-end manner. The efficacy of the proposed learning algorithm is demonstrated via experiments on clustering MNIST images and on the challenging task of real-world multi-people pose estimation.

PDF [BibTex]

PDF [BibTex]


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Three-D Safari: Learning to Estimate Zebra Pose, Shape, and Texture from Images "In the Wild"

Zuffi, S., Kanazawa, A., Berger-Wolf, T., Black, M. J.

In International Conference on Computer Vision, October 2019 (inproceedings)

Abstract
We present the first method to perform automatic 3D pose, shape and texture capture of animals from images acquired in-the-wild. In particular, we focus on the problem of capturing 3D information about Grevy's zebras from a collection of images. The Grevy's zebra is one of the most endangered species in Africa, with only a few thousand individuals left. Capturing the shape and pose of these animals can provide biologists and conservationists with information about animal health and behavior. In contrast to research on human pose, shape and texture estimation, training data for endangered species is limited, the animals are in complex natural scenes with occlusion, they are naturally camouflaged, travel in herds, and look similar to each other. To overcome these challenges, we integrate the recent SMAL animal model into a network-based regression pipeline, which we train end-to-end on synthetically generated images with pose, shape, and background variation. Going beyond state-of-the-art methods for human shape and pose estimation, our method learns a shape space for zebras during training. Learning such a shape space from images using only a photometric loss is novel, and the approach can be used to learn shape in other settings with limited 3D supervision. Moreover, we couple 3D pose and shape prediction with the task of texture synthesis, obtaining a full texture map of the animal from a single image. We show that the predicted texture map allows a novel per-instance unsupervised optimization over the network features. This method, SMALST (SMAL with learned Shape and Texture) goes beyond previous work, which assumed manual keypoints and/or segmentation, to regress directly from pixels to 3D animal shape, pose and texture. Code and data are available at https://github.com/silviazuffi/smalst

code pdf supmat iccv19 presentation Project Page [BibTex]


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Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles

Saini, N., Price, E., Tallamraju, R., Enficiaud, R., Ludwig, R., Martinović, I., Ahmad, A., Black, M.

In International Conference on Computer Vision, October 2019 (inproceedings) Accepted

Abstract
Capturing human motion in natural scenarios means moving motion capture out of the lab and into the wild. Typical approaches rely on fixed, calibrated, cameras and reflective markers on the body, significantly limiting the motions that can be captured. To make motion capture truly unconstrained, we describe the first fully autonomous outdoor capture system based on flying vehicles. We use multiple micro-aerial-vehicles(MAVs), each equipped with a monocular RGB camera, an IMU, and a GPS receiver module. These detect the person, optimize their position, and localize themselves approximately. We then develop a markerless motion capture method that is suitable for this challenging scenario with a distant subject, viewed from above, with approximately calibrated and moving cameras. We combine multiple state-of-the-art 2D joint detectors with a 3D human body model and a powerful prior on human pose. We jointly optimize for 3D body pose and camera pose to robustly fit the 2D measurements. To our knowledge, this is the first successful demonstration of outdoor, full-body, markerless motion capture from autonomous flying vehicles.

Code Data Video Paper Manuscript Project Page [BibTex]


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Active Perception based Formation Control for Multiple Aerial Vehicles

Tallamraju, R., Price, E., Ludwig, R., Karlapalem, K., Bülthoff, H. H., Black, M. J., Ahmad, A.

IEEE Robotics and Automation Letters, Robotics and Automation Letters, 4(4):4491-4498, IEEE, October 2019 (article)

Abstract
We present a novel robotic front-end for autonomous aerial motion-capture (mocap) in outdoor environments. In previous work, we presented an approach for cooperative detection and tracking (CDT) of a subject using multiple micro-aerial vehicles (MAVs). However, it did not ensure optimal view-point configurations of the MAVs to minimize the uncertainty in the person's cooperatively tracked 3D position estimate. In this article, we introduce an active approach for CDT. In contrast to cooperatively tracking only the 3D positions of the person, the MAVs can actively compute optimal local motion plans, resulting in optimal view-point configurations, which minimize the uncertainty in the tracked estimate. We achieve this by decoupling the goal of active tracking into a quadratic objective and non-convex constraints corresponding to angular configurations of the MAVs w.r.t. the person. We derive this decoupling using Gaussian observation model assumptions within the CDT algorithm. We preserve convexity in optimization by embedding all the non-convex constraints, including those for dynamic obstacle avoidance, as external control inputs in the MPC dynamics. Multiple real robot experiments and comparisons involving 3 MAVs in several challenging scenarios are presented.

pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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AMASS: Archive of Motion Capture as Surface Shapes

Mahmood, N., Ghorbani, N., Troje, N. F., Pons-Moll, G., Black, M. J.

International Conference on Computer Vision, October 2019 (conference)

Abstract
Large datasets are the cornerstone of recent advances in computer vision using deep learning. In contrast, existing human motion capture (mocap) datasets are small and the motions limited, hampering progress on learning models of human motion. While there are many different datasets available, they each use a different parameterization of the body, making it difficult to integrate them into a single meta dataset. To address this, we introduce AMASS, a large and varied database of human motion that unifies 15 different optical marker-based mocap datasets by representing them within a common framework and parameterization. We achieve this using a new method, MoSh++, that converts mocap data into realistic 3D human meshes represented by a rigged body model. Here we use SMPL [26], which is widely used and provides a standard skeletal representation as well as a fully rigged surface mesh. The method works for arbitrary marker-sets, while recovering soft-tissue dynamics and realistic hand motion. We evaluate MoSh++ and tune its hyper-parameters using a new dataset of 4D body scans that are jointly recorded with marker-based mocap. The consistent representation of AMASS makes it readily useful for animation, visualization, and generating training data for deep learning. Our dataset is significantly richer than previous human motion collections, having more than 40 hours of motion data, spanning over 300 subjects, more than 11000 motions, and is available for research at https://amass.is.tue.mpg.de/.

code pdf suppl arxiv project website video poster AMASS_Poster [BibTex]


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Method for providing a three dimensional body model

Loper, M., Mahmood, N., Black, M.

September 2019, U.S.~Patent 10,417,818 (misc)

Abstract
A method for providing a three-dimensional body model which may be applied for an animation, based on a moving body, wherein the method comprises providing a parametric three-dimensional body model, which allows shape and pose variations; applying a standard set of body markers; optimizing the set of body markers by generating an additional set of body markers and applying the same for providing 3D coordinate marker signals for capturing shape and pose of the body and dynamics of soft tissue; and automatically providing an animation by processing the 3D coordinate marker signals in order to provide a personalized three-dimensional body model, based on estimated shape and an estimated pose of the body by means of predicted marker locations.

MoSh Project pdf [BibTex]


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Learning to Train with Synthetic Humans

Hoffmann, D. T., Tzionas, D., Black, M. J., Tang, S.

In German Conference on Pattern Recognition (GCPR), September 2019 (inproceedings)

Abstract
Neural networks need big annotated datasets for training. However, manual annotation can be too expensive or even unfeasible for certain tasks, like multi-person 2D pose estimation with severe occlusions. A remedy for this is synthetic data with perfect ground truth. Here we explore two variations of synthetic data for this challenging problem; a dataset with purely synthetic humans, as well as a real dataset augmented with synthetic humans. We then study which approach better generalizes to real data, as well as the influence of virtual humans in the training loss. We observe that not all synthetic samples are equally informative for training, while the informative samples are different for each training stage. To exploit this observation, we employ an adversarial student-teacher framework; the teacher improves the student by providing the hardest samples for its current state as a challenge. Experiments show that this student-teacher framework outperforms all our baselines.

pdf suppl poster link (url) [BibTex]

pdf suppl poster link (url) [BibTex]


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The Influence of Visual Perspective on Body Size Estimation in Immersive Virtual Reality

Thaler, A., Pujades, S., Stefanucci, J. K., Creem-Regehr, S. H., Tesch, J., Black, M. J., Mohler, B. J.

In ACM Symposium on Applied Perception, September 2019 (inproceedings)

Abstract
The creation of realistic self-avatars that users identify with is important for many virtual reality applications. However, current approaches for creating biometrically plausible avatars that represent a particular individual require expertise and are time-consuming. We investigated the visual perception of an avatar’s body dimensions by asking males and females to estimate their own body weight and shape on a virtual body using a virtual reality avatar creation tool. In a method of adjustment task, the virtual body was presented in an HTC Vive head-mounted display either co-located with (first-person perspective) or facing (third-person perspective) the participants. Participants adjusted the body weight and dimensions of various body parts to match their own body shape and size. Both males and females underestimated their weight by 10-20% in the virtual body, but the estimates of the other body dimensions were relatively accurate and within a range of ±6%. There was a stronger influence of visual perspective on the estimates for males, but this effect was dependent on the amount of control over the shape of the virtual body, indicating that the results might be caused by where in the body the weight changes expressed themselves. These results suggest that this avatar creation tool could be used to allow participants to make a relatively accurate self-avatar in terms of adjusting body part dimensions, but not weight, and that the influence of visual perspective and amount of control needed over the body shape are likely gender-specific.

pdf [BibTex]

pdf [BibTex]


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3D Morphable Face Models - Past, Present and Future

Egger, B., Smith, W. A. P., Tewari, A., Wuhrer, S., Zollhoefer, M., Beeler, T., Bernard, F., Bolkart, T., Kortylewski, A., Romdhani, S., Theobalt, C., Blanz, V., Vetter, T.

arxiv preprint arXiv:1909.01815, September 2019 (article)

Abstract
In this paper, we provide a detailed survey of 3D Morphable Face Models over the 20 years since they were first proposed. The challenges in building and applying these models, namely capture, modeling, image formation,and image analysis, are still active research topics, and we review the state-of-the-art in each of these areas. We also look ahead, identifying unsolved challenges, proposing directions for future research and highlighting the broad range of current and future applications.

paper project page [BibTex]

paper project page [BibTex]


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Motion Planning for Multi-Mobile-Manipulator Payload Transport Systems

Tallamraju, R., Salunkhe, D., Rajappa, S., Ahmad, A., Karlapalem, K., Shah, S. V.

In 15th IEEE International Conference on Automation Science and Engineering, IEEE, August 2019 (inproceedings) Accepted

[BibTex]

[BibTex]


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Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

Ranjan, A., Jampani, V., Balles, L., Kim, K., Sun, D., Wulff, J., Black, M. J.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2019 (inproceedings)

Abstract
We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions. Our key insight is that these four fundamental vision problems are coupled through geometric constraints. Consequently, learning to solve them together simplifies the problem because the solutions can reinforce each other. We go beyond previous work by exploiting geometry more explicitly and segmenting the scene into static and moving regions. To that end, we introduce Competitive Collaboration, a framework that facilitates the coordinated training of multiple specialized neural networks to solve complex problems. Competitive Collaboration works much like expectation-maximization, but with neural networks that act as both competitors to explain pixels that correspond to static or moving regions, and as collaborators through a moderator that assigns pixels to be either static or independently moving. Our novel method integrates all these problems in a common framework and simultaneously reasons about the segmentation of the scene into moving objects and the static background, the camera motion, depth of the static scene structure, and the optical flow of moving objects. Our model is trained without any supervision and achieves state-of-the-art performance among joint unsupervised methods on all sub-problems.

Paper link (url) Project Page Project Page [BibTex]


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Local Temporal Bilinear Pooling for Fine-grained Action Parsing

Zhang, Y., Tang, S., Muandet, K., Jarvers, C., Neumann, H.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2019 (inproceedings)

Abstract
Fine-grained temporal action parsing is important in many applications, such as daily activity understanding, human motion analysis, surgical robotics and others requiring subtle and precise operations in a long-term period. In this paper we propose a novel bilinear pooling operation, which is used in intermediate layers of a temporal convolutional encoder-decoder net. In contrast to other work, our proposed bilinear pooling is learnable and hence can capture more complex local statistics than the conventional counterpart. In addition, we introduce exact lower-dimension representations of our bilinear forms, so that the dimensionality is reduced with neither information loss nor extra computation. We perform intensive experiments to quantitatively analyze our model and show the superior performances to other state-of-the-art work on various datasets.

Code video demo pdf link (url) [BibTex]

Code video demo pdf link (url) [BibTex]


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Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision

Sanyal, S., Bolkart, T., Feng, H., Black, M. J.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2019 (inproceedings)

Abstract
The estimation of 3D face shape from a single image must be robust to variations in lighting, head pose, expression, facial hair, makeup, and occlusions. Robustness requires a large training set of in-the-wild images, which by construction, lack ground truth 3D shape. To train a network without any 2D-to-3D supervision, we present RingNet, which learns to compute 3D face shape from a single image. Our key observation is that an individual’s face shape is constant across images, regardless of expression, pose, lighting, etc. RingNet leverages multiple images of a person and automatically detected 2D face features. It uses a novel loss that encourages the face shape to be similar when the identity is the same and different for different people. We achieve invariance to expression by representing the face using the FLAME model. Once trained, our method takes a single image and outputs the parameters of FLAME, which can be readily animated. Additionally we create a new database of faces “not quite in-the-wild” (NoW) with 3D head scans and high-resolution images of the subjects in a wide variety of conditions. We evaluate publicly available methods and find that RingNet is more accurate than methods that use 3D supervision. The dataset, model, and results are available for research purposes.

code pdf preprint link (url) Project Page [BibTex]


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Learning Joint Reconstruction of Hands and Manipulated Objects

Hasson, Y., Varol, G., Tzionas, D., Kalevatykh, I., Black, M. J., Laptev, I., Schmid, C.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2019 (inproceedings)

Abstract
Estimating hand-object manipulations is essential for interpreting and imitating human actions. Previous work has made significant progress towards reconstruction of hand poses and object shapes in isolation. Yet, reconstructing hands and objects during manipulation is a more challenging task due to significant occlusions of both the hand and object. While presenting challenges, manipulations may also simplify the problem since the physics of contact restricts the space of valid hand-object configurations. For example, during manipulation, the hand and object should be in contact but not interpenetrate. In this work, we regularize the joint reconstruction of hands and objects with manipulation constraints. We present an end-to-end learnable model that exploits a novel contact loss that favors physically plausible hand-object constellations. Our approach improves grasp quality metrics over baselines, using RGB images as input. To train and evaluate the model, we also propose a new large-scale synthetic dataset, ObMan, with hand-object manipulations. We demonstrate the transferability of ObMan-trained models to real data.

pdf suppl poster link (url) Project Page Project Page [BibTex]

pdf suppl poster link (url) Project Page Project Page [BibTex]


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Expressive Body Capture: 3D Hands, Face, and Body from a Single Image

Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A. A. A., Tzionas, D., Black, M. J.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2019 (inproceedings)

Abstract
To facilitate the analysis of human actions, interactions and emotions, we compute a 3D model of human body pose, hand pose, and facial expression from a single monocular image. To achieve this, we use thousands of 3D scans to train a new, unified, 3D model of the human body, SMPL-X, that extends SMPL with fully articulated hands and an expressive face. Learning to regress the parameters of SMPL-X directly from images is challenging without paired images and 3D ground truth. Consequently, we follow the approach of SMPLify, which estimates 2D features and then optimizes model parameters to fit the features. We improve on SMPLify in several significant ways: (1) we detect 2D features corresponding to the face, hands, and feet and fit the full SMPL-X model to these; (2) we train a new neural network pose prior using a large MoCap dataset; (3) we define a new interpenetration penalty that is both fast and accurate; (4) we automatically detect gender and the appropriate body models (male, female, or neutral); (5) our PyTorch implementation achieves a speedup of more than 8x over Chumpy. We use the new method, SMPLify-X, to fit SMPL-X to both controlled images and images in the wild. We evaluate 3D accuracy on a new curated dataset comprising 100 images with pseudo ground-truth. This is a step towards automatic expressive human capture from monocular RGB data. The models, code, and data are available for research purposes at https://smpl-x.is.tue.mpg.de.

video code pdf suppl poster link (url) Project Page [BibTex]


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Capture, Learning, and Synthesis of 3D Speaking Styles

Cudeiro, D., Bolkart, T., Laidlaw, C., Ranjan, A., Black, M. J.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2019 (inproceedings)

Abstract
Audio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, models, and standard evaluation metrics. To address this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio from 12 speakers. We then train a neural network on our dataset that factors identity from facial motion. The learned model, VOCA (Voice Operated Character Animation) takes any speech signal as input—even speech in languages other than English—and realistically animates a wide range of adult faces. Conditioning on subject labels during training allows the model to learn a variety of realistic speaking styles. VOCA also provides animator controls to alter speaking style, identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball rotations) during animation. To our knowledge, VOCA is the only realistic 3D facial animation model that is readily applicable to unseen subjects without retargeting. This makes VOCA suitable for tasks like in-game video, virtual reality avatars, or any scenario in which the speaker, speech, or language is not known in advance. We make the dataset and model available for research purposes at http://voca.is.tue.mpg.de.

code Project Page video paper [BibTex]

code Project Page video paper [BibTex]


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Learning and Tracking the 3D Body Shape of Freely Moving Infants from RGB-D sequences

Hesse, N., Pujades, S., Black, M., Arens, M., Hofmann, U., Schroeder, S.

Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019 (article)

Abstract
Statistical models of the human body surface are generally learned from thousands of high-quality 3D scans in predefined poses to cover the wide variety of human body shapes and articulations. Acquisition of such data requires expensive equipment, calibration procedures, and is limited to cooperative subjects who can understand and follow instructions, such as adults. We present a method for learning a statistical 3D Skinned Multi-Infant Linear body model (SMIL) from incomplete, low-quality RGB-D sequences of freely moving infants. Quantitative experiments show that SMIL faithfully represents the RGB-D data and properly factorizes the shape and pose of the infants. To demonstrate the applicability of SMIL, we fit the model to RGB-D sequences of freely moving infants and show, with a case study, that our method captures enough motion detail for General Movements Assessment (GMA), a method used in clinical practice for early detection of neurodevelopmental disorders in infants. SMIL provides a new tool for analyzing infant shape and movement and is a step towards an automated system for GMA.

pdf Journal DOI [BibTex]

pdf Journal DOI [BibTex]


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Perceptual Effects of Inconsistency in Human Animations

Kenny, S., Mahmood, N., Honda, C., Black, M. J., Troje, N. F.

ACM Trans. Appl. Percept., 16(1):2:1-2:18, Febuary 2019 (article)

Abstract
The individual shape of the human body, including the geometry of its articulated structure and the distribution of weight over that structure, influences the kinematics of a person’s movements. How sensitive is the visual system to inconsistencies between shape and motion introduced by retargeting motion from one person onto the shape of another? We used optical motion capture to record five pairs of male performers with large differences in body weight, while they pushed, lifted, and threw objects. From these data, we estimated both the kinematics of the actions as well as the performer’s individual body shape. To obtain consistent and inconsistent stimuli, we created animated avatars by combining the shape and motion estimates from either a single performer or from different performers. Using these stimuli we conducted three experiments in an immersive virtual reality environment. First, a group of participants detected which of two stimuli was inconsistent. Performance was very low, and results were only marginally significant. Next, a second group of participants rated perceived attractiveness, eeriness, and humanness of consistent and inconsistent stimuli, but these judgements of animation characteristics were not affected by consistency of the stimuli. Finally, a third group of participants rated properties of the objects rather than of the performers. Here, we found strong influences of shape-motion inconsistency on perceived weight and thrown distance of objects. This suggests that the visual system relies on its knowledge of shape and motion and that these components are assimilated into an altered perception of the action outcome. We propose that the visual system attempts to resist inconsistent interpretations of human animations. Actions involving object manipulations present an opportunity for the visual system to reinterpret the introduced inconsistencies as a change in the dynamics of an object rather than as an unexpected combination of body shape and body motion.

publisher pdf DOI [BibTex]

publisher pdf DOI [BibTex]


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Perceiving Systems (2016-2018)
Scientific Advisory Board Report, 2019 (misc)

pdf [BibTex]

pdf [BibTex]


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The Virtual Caliper: Rapid Creation of Metrically Accurate Avatars from 3D Measurements

Pujades, S., Mohler, B., Thaler, A., Tesch, J., Mahmood, N., Hesse, N., Bülthoff, H. H., Black, M. J.

IEEE Transactions on Visualization and Computer Graphics, 25, pages: 1887,1897, IEEE, 2019 (article)

Abstract
Creating metrically accurate avatars is important for many applications such as virtual clothing try-on, ergonomics, medicine, immersive social media, telepresence, and gaming. Creating avatars that precisely represent a particular individual is challenging however, due to the need for expensive 3D scanners, privacy issues with photographs or videos, and difficulty in making accurate tailoring measurements. We overcome these challenges by creating “The Virtual Caliper”, which uses VR game controllers to make simple measurements. First, we establish what body measurements users can reliably make on their own body. We find several distance measurements to be good candidates and then verify that these are linearly related to 3D body shape as represented by the SMPL body model. The Virtual Caliper enables novice users to accurately measure themselves and create an avatar with their own body shape. We evaluate the metric accuracy relative to ground truth 3D body scan data, compare the method quantitatively to other avatar creation tools, and perform extensive perceptual studies. We also provide a software application to the community that enables novices to rapidly create avatars in fewer than five minutes. Not only is our approach more rapid than existing methods, it exports a metrically accurate 3D avatar model that is rigged and skinned.

Project Page IEEE Open Access IEEE Open Access PDF DOI [BibTex]

Project Page IEEE Open Access IEEE Open Access PDF DOI [BibTex]


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Resisting Adversarial Attacks using Gaussian Mixture Variational Autoencoders

Ghosh, P., Losalka, A., Black, M. J.

In Proc. AAAI, 2019 (inproceedings)

Abstract
Susceptibility of deep neural networks to adversarial attacks poses a major theoretical and practical challenge. All efforts to harden classifiers against such attacks have seen limited success till now. Two distinct categories of samples against which deep neural networks are vulnerable, ``adversarial samples" and ``fooling samples", have been tackled separately so far due to the difficulty posed when considered together. In this work, we show how one can defend against them both under a unified framework. Our model has the form of a variational autoencoder with a Gaussian mixture prior on the latent variable, such that each mixture component corresponds to a single class. We show how selective classification can be performed using this model, thereby causing the adversarial objective to entail a conflict. The proposed method leads to the rejection of adversarial samples instead of misclassification, while maintaining high precision and recall on test data. It also inherently provides a way of learning a selective classifier in a semi-supervised scenario, which can similarly resist adversarial attacks. We further show how one can reclassify the detected adversarial samples by iterative optimization.

link (url) Project Page [BibTex]


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From Variational to Deterministic Autoencoders

Ghosh*, P., Sajjadi*, M. S. M., Vergari, A., Black, M. J., Schölkopf, B.

2019, *equal contribution (conference) Submitted

Abstract
Variational Autoencoders (VAEs) provide a theoretically-backed framework for deep generative models. However, they often produce “blurry” images, which is linked to their training objective. Sampling in the most popular implementation, the Gaussian VAE, can be interpreted as simply injecting noise to the input of a deterministic decoder. In practice, this simply enforces a smooth latent space structure. We challenge the adoption of the full VAE framework on this specific point in favor of a simpler, deterministic one. Specifically, we investigate how substituting stochasticity with other explicit and implicit regularization schemes can lead to a meaningful latent space without having to force it to conform to an arbitrarily chosen prior. To retrieve a generative mechanism for sampling new data points, we propose to employ an efficient ex-post density estimation step that can be readily adopted both for the proposed deterministic autoencoders as well as to improve sample quality of existing VAEs. We show in a rigorous empirical study that regularized deterministic autoencoding achieves state-of-the-art sample quality on the common MNIST, CIFAR-10 and CelebA datasets.

arXiv [BibTex]

2013


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Learning People Detectors for Tracking in Crowded Scenes

Tang, S., Andriluka, M., Milan, A., Schindler, K., Roth, S., Schiele, B.

In 2013 IEEE International Conference on Computer Vision, pages: 1049-1056, IEEE, December 2013 (inproceedings)

PDF DOI [BibTex]

2013

PDF DOI [BibTex]


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Branch&Rank for Efficient Object Detection

Lehmann, A., Gehler, P., VanGool, L.

International Journal of Computer Vision, Springer, December 2013 (article)

Abstract
Ranking hypothesis sets is a powerful concept for efficient object detection. In this work, we propose a branch&rank scheme that detects objects with often less than 100 ranking operations. This efficiency enables the use of strong and also costly classifiers like non-linear SVMs with RBF-TeX kernels. We thereby relieve an inherent limitation of branch&bound methods as bounds are often not tight enough to be effective in practice. Our approach features three key components: a ranking function that operates on sets of hypotheses and a grouping of these into different tasks. Detection efficiency results from adaptively sub-dividing the object search space into decreasingly smaller sets. This is inherited from branch&bound, while the ranking function supersedes a tight bound which is often unavailable (except for rather limited function classes). The grouping makes the system effective: it separates image classification from object recognition, yet combines them in a single formulation, phrased as a structured SVM problem. A novel aspect of branch&rank is that a better ranking function is expected to decrease the number of classifier calls during detection. We use the VOC’07 dataset to demonstrate the algorithmic properties of branch&rank.

pdf link (url) [BibTex]

pdf link (url) [BibTex]


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Strong Appearance and Expressive Spatial Models for Human Pose Estimation

Pishchulin, L., Andriluka, M., Gehler, P., Schiele, B.

In International Conference on Computer Vision (ICCV), pages: 3487 - 3494 , IEEE, December 2013 (inproceedings)

Abstract
Typical approaches to articulated pose estimation combine spatial modelling of the human body with appearance modelling of body parts. This paper aims to push the state-of-the-art in articulated pose estimation in two ways. First we explore various types of appearance representations aiming to substantially improve the body part hypotheses. And second, we draw on and combine several recently proposed powerful ideas such as more flexible spatial models as well as image-conditioned spatial models. In a series of experiments we draw several important conclusions: (1) we show that the proposed appearance representations are complementary; (2) we demonstrate that even a basic tree-structure spatial human body model achieves state-of-the-art performance when augmented with the proper appearance representation; and (3) we show that the combination of the best performing appearance model with a flexible image-conditioned spatial model achieves the best result, significantly improving over the state of the art, on the "Leeds Sports Poses'' and "Parse'' benchmarks.

pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


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Methods and Applications for Distance Based ANN Training

Lassner, C., Lienhart, R.

In IEEE International Conference on Machine Learning and Applications (ICMLA), December 2013 (inproceedings)

Abstract
Feature learning has the aim to take away the hassle of hand-designing features for machine learning tasks. Since the feature design process is tedious and requires a lot of experience, an automated solution is of great interest. However, an important problem in this field is that usually no objective values are available to fit a feature learning function to. Artificial Neural Networks are a sufficiently flexible tool for function approximation to be able to avoid this problem. We show how the error function of an ANN can be modified such that it works solely with objective distances instead of objective values. We derive the adjusted rules for backpropagation through networks with arbitrary depths and include practical considera- tions that must be taken into account to apply difference based learning successfully. On all three benchmark datasets we use, linear SVMs trained on automatically learned ANN features outperform RBF kernel SVMs trained on the raw data. This can be achieved in a feature space with up to only a tenth of dimensions of the number of original data dimensions. We conclude our work with two experiments on distance based ANN training in two further fields: data visualization and outlier detection.

pdf [BibTex]

pdf [BibTex]


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Extracting Postural Synergies for Robotic Grasping

Romero, J., Feix, T., Ek, C., Kjellstrom, H., Kragic, D.

Robotics, IEEE Transactions on, 29(6):1342-1352, December 2013 (article)

[BibTex]

[BibTex]


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Understanding High-Level Semantics by Modeling Traffic Patterns

Zhang, H., Geiger, A., Urtasun, R.

In International Conference on Computer Vision, pages: 3056-3063, Sydney, Australia, December 2013 (inproceedings)

Abstract
In this paper, we are interested in understanding the semantics of outdoor scenes in the context of autonomous driving. Towards this goal, we propose a generative model of 3D urban scenes which is able to reason not only about the geometry and objects present in the scene, but also about the high-level semantics in the form of traffic patterns. We found that a small number of patterns is sufficient to model the vast majority of traffic scenes and show how these patterns can be learned. As evidenced by our experiments, this high-level reasoning significantly improves the overall scene estimation as well as the vehicle-to-lane association when compared to state-of-the-art approaches. All data and code will be made available upon publication.

pdf [BibTex]

pdf [BibTex]


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A Non-parametric Bayesian Network Prior of Human Pose

Lehrmann, A. M., Gehler, P., Nowozin, S.

In Proceedings IEEE Conf. on Computer Vision (ICCV), pages: 1281-1288, December 2013 (inproceedings)

Abstract
Having a sensible prior of human pose is a vital ingredient for many computer vision applications, including tracking and pose estimation. While the application of global non-parametric approaches and parametric models has led to some success, finding the right balance in terms of flexibility and tractability, as well as estimating model parameters from data has turned out to be challenging. In this work, we introduce a sparse Bayesian network model of human pose that is non-parametric with respect to the estimation of both its graph structure and its local distributions. We describe an efficient sampling scheme for our model and show its tractability for the computation of exact log-likelihoods. We empirically validate our approach on the Human 3.6M dataset and demonstrate superior performance to global models and parametric networks. We further illustrate our model's ability to represent and compose poses not present in the training set (compositionality) and describe a speed-accuracy trade-off that allows realtime scoring of poses.

Project page pdf DOI Project Page [BibTex]

Project page pdf DOI Project Page [BibTex]


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Towards understanding action recognition

Jhuang, H., Gall, J., Zuffi, S., Schmid, C., Black, M. J.

In IEEE International Conference on Computer Vision (ICCV), pages: 3192-3199, IEEE, Sydney, Australia, December 2013 (inproceedings)

Abstract
Although action recognition in videos is widely studied, current methods often fail on real-world datasets. Many recent approaches improve accuracy and robustness to cope with challenging video sequences, but it is often unclear what affects the results most. This paper attempts to provide insights based on a systematic performance evaluation using thoroughly-annotated data of human actions. We annotate human Joints for the HMDB dataset (J-HMDB). This annotation can be used to derive ground truth optical flow and segmentation. We evaluate current methods using this dataset and systematically replace the output of various algorithms with ground truth. This enables us to discover what is important – for example, should we work on improving flow algorithms, estimating human bounding boxes, or enabling pose estimation? In summary, we find that highlevel pose features greatly outperform low/mid level features; in particular, pose over time is critical, but current pose estimation algorithms are not yet reliable enough to provide this information. We also find that the accuracy of a top-performing action recognition framework can be greatly increased by refining the underlying low/mid level features; this suggests it is important to improve optical flow and human detection algorithms. Our analysis and JHMDB dataset should facilitate a deeper understanding of action recognition algorithms.

Website Errata Poster Paper Slides DOI Project Page Project Page Project Page [BibTex]

Website Errata Poster Paper Slides DOI Project Page Project Page Project Page [BibTex]


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Mixing Decoded Cursor Velocity and Position from an Offline Kalman Filter Improves Cursor Control in People with Tetraplegia

Homer, M., Harrison, M., Black, M. J., Perge, J., Cash, S., Friehs, G., Hochberg, L.

In 6th International IEEE EMBS Conference on Neural Engineering, pages: 715-718, San Diego, November 2013 (inproceedings)

Abstract
Kalman filtering is a common method to decode neural signals from the motor cortex. In clinical research investigating the use of intracortical brain computer interfaces (iBCIs), the technique enabled people with tetraplegia to control assistive devices such as a computer or robotic arm directly from their neural activity. For reaching movements, the Kalman filter typically estimates the instantaneous endpoint velocity of the control device. Here, we analyzed attempted arm/hand movements by people with tetraplegia to control a cursor on a computer screen to reach several circular targets. A standard velocity Kalman filter is enhanced to additionally decode for the cursor’s position. We then mix decoded velocity and position to generate cursor movement commands. We analyzed data, offline, from two participants across six sessions. Root mean squared error between the actual and estimated cursor trajectory improved by 12.2 ±10.5% (pairwise t-test, p<0.05) as compared to a standard velocity Kalman filter. The findings suggest that simultaneously decoding for intended velocity and position and using them both to generate movement commands can improve the performance of iBCIs.

pdf Project Page [BibTex]

pdf Project Page [BibTex]


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Markov Random Field Modeling, Inference & Learning in Computer Vision & Image Understanding: A Survey

Wang, C., Komodakis, N., Paragios, N.

Computer Vision and Image Understanding (CVIU), 117(11):1610-1627, November 2013 (article)

Abstract
In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in computer vision and image understanding, with respect to the modeling, the inference and the learning. While MRFs were introduced into the computer vision field about two decades ago, they started to become a ubiquitous tool for solving visual perception problems around the turn of the millennium following the emergence of efficient inference methods. During the past decade, a variety of MRF models as well as inference and learning methods have been developed for addressing numerous low, mid and high-level vision problems. While most of the literature concerns pairwise MRFs, in recent years we have also witnessed significant progress in higher-order MRFs, which substantially enhances the expressiveness of graph-based models and expands the domain of solvable problems. This survey provides a compact and informative summary of the major literature in this research topic.

Publishers site pdf [BibTex]

Publishers site pdf [BibTex]


no image
Multi-robot cooperative spherical-object tracking in 3D space based on particle filters

Ahmad, A., Lima, P.

Robotics and Autonomous Systems, 61(10):1084-1093, October 2013 (article)

Abstract
This article presents a cooperative approach for tracking a moving spherical object in 3D space by a team of mobile robots equipped with sensors, in a highly dynamic environment. The tracker’s core is a particle filter, modified to handle, within a single unified framework, the problem of complete or partial occlusion for some of the involved mobile sensors, as well as inconsistent estimates in the global frame among sensors, due to observation errors and/or self-localization uncertainty. We present results supporting our approach by applying it to a team of real soccer robots tracking a soccer ball, including comparison with ground truth.

DOI [BibTex]

DOI [BibTex]


no image
Multi-Robot Cooperative Object Tracking Based on Particle Filters

Ahmad, A., Lima, P.

In Robotics and Autonomous Systems, 61(10):1084-1093, October 2013 (inproceedings)

Abstract
This article presents a cooperative approach for tracking a moving object by a team of mobile robots equipped with sensors, in a highly dynamic environment. The tracker’s core is a particle filter, modified to handle, within a single unified framework, the problem of complete or partial occlusion for some of the involved mobile sensors, as well as inconsistent estimates in the global frame among sensors, due to observation errors and/or self-localization uncertainty. We present results supporting our approach by applying it to a team of real soccer robots tracking a soccer ball.

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Puppet Flow

Zuffi, S., Black, M. J.

(7), Max Planck Institute for Intelligent Systems, October 2013 (techreport)

Abstract
We introduce Puppet Flow (PF), a layered model describing the optical flow of a person in a video sequence. We consider video frames composed by two layers: a foreground layer corresponding to a person, and background. We model the background as an affine flow field. The foreground layer, being a moving person, requires reasoning about the articulated nature of the human body. We thus represent the foreground layer with the Deformable Structures model (DS), a parametrized 2D part-based human body representation. We call the motion field defined through articulated motion and deformation of the DS model, a Puppet Flow. By exploiting the DS representation, Puppet Flow is a parametrized optical flow field, where parameters are the person's pose, gender and body shape.

pdf Project Page Project Page [BibTex]

pdf Project Page Project Page [BibTex]


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D2.1.4 RoCKIn@Work - Innovation in Mobile Industrial Manipulation Competition Design, Rule Book, and Scenario Construction

Ahmad, A., Awaad, I., Amigoni, F., Berghofer, J., Bischoff, R., Bonarini, A., Dwiputra, R., Hegger, F., Hochgeschwender, N., Iocchi, L., Kraetzschmar, G., Lima, P., Matteucci, M., Nardi, D., Schneider, S.

(FP7-ICT-601012 Revision 0.7), RoCKIn - Robot Competitions Kick Innovation in Cognitive Systems and Robotics, sep 2013 (techreport)

Abstract
RoCKIn is a EU-funded project aiming to foster scientific progress and innovation in cognitive systems and robotics through the design and implementation of competitions. An additional objective of RoCKIn is to increase public awareness of the current state-of-the-art in robotics in Europe and to demonstrate the innovation potential of robotics applications for solving societal challenges and improving the competitiveness of Europe in the global markets. In order to achieve these objectives, RoCKIn develops two competitions, one for domestic service robots (RoCKIn@Home) and one for industrial robots in factories (RoCKIn-@Work). These competitions are designed around challenges that are based on easy-to-communicate and convincing user stories, which catch the interest of both the general public and the scientifc community. The latter is in particular interested in solving open scientific challenges and to thoroughly assess, compare, and evaluate the developed approaches with competing ones. To allow this to happen, the competitions are designed to meet the requirements of benchmarking procedures and good experimental methods. The integration of benchmarking technology with the competition concept is one of the main objectives of RoCKIn. This document describes the first version of the RoCKIn@Work competition, which will be held for the first time in 2014. The first chapter of the document gives a brief overview, outlining the purpose and objective of the competition, the methodological approach taken by the RoCKIn project, the user story upon which the competition is based, the structure and organization of the competition, and the commonalities and differences with the RoboCup@Work competition, which served as inspiration for RoCKIn@Work. The second chapter provides details on the user story and analyzes the scientific and technical challenges it poses. Consecutive chapters detail the competition scenario, the competition design, and the organization of the competition. The appendices contain information on a library of functionalities, which we believe are needed, or at least useful, for building competition entries, details on the scenario construction, and a detailed account of the benchmarking infrastructure needed — and provided by RoCKIn.

[BibTex]

[BibTex]


no image
D2.1.1 RoCKIn@Home - A Competition for Domestic Service Robots Competition Design, Rule Book, and Scenario Construction

Ahmad, A., Awaad, I., Amigoni, F., Berghofer, J., Bischoff, R., Bonarini, A., Dwiputra, R., Hegger, F., Hochgeschwender, N., Iocchi, L., Kraetzschmar, G., Lima, P., Matteucci, M., Nardi, D., Schneider, S.

(FP7-ICT-601012 Revision 0.7), RoCKIn - Robot Competitions Kick Innovation in Cognitive Systems and Robotics, sep 2013 (techreport)

Abstract
RoCKIn is a EU-funded project aiming to foster scientific progress and innovation in cognitive systems and robotics through the design and implementation of competitions. An additional objective of RoCKIn is to increase public awareness of the current state-of-the-art in robotics in Europe and to demonstrate the innovation potential of robotics applications for solving societal challenges and improving the competitiveness of Europe in the global markets. In order to achieve these objectives, RoCKIn develops two competitions, one for domestic service robots (RoCKIn@Home) and one for industrial robots in factories (RoCKIn-@Work). These competitions are designed around challenges that are based on easy-to-communicate and convincing user stories, which catch the interest of both the general public and the scientifc community. The latter is in particular interested in solving open scientific challenges and to thoroughly assess, compare, and evaluate the developed approaches with competing ones. To allow this to happen, the competitions are designed to meet the requirements of benchmarking procedures and good experimental methods. The integration of benchmarking technology with the competition concept is one of the main objectives of RoCKIn. This document describes the first version of the RoCKIn@Home competition, which will be held for the first time in 2014. The first chapter of the document gives a brief overview, outlining the purpose and objective of the competition, the methodological approach taken by the RoCKIn project, the user story upon which the competition is based, the structure and organization of the competition, and the commonalities and differences with the RoboCup@Home competition, which served as inspiration for RoCKIn@Home. The second chapter provides details on the user story and analyzes the scientific and technical challenges it poses. Consecutive chapters detail the competition scenario, the competition design, and the organization of the competition. The appendices contain information on a library of functionalities, which we believe are needed, or at least useful, for building competition entries, details on the scenario construction, and a detailed account of the benchmarking infrastructure needed — and provided by RoCKIn.

[BibTex]

[BibTex]


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Distribution Fields with Adaptive Kernels for Large Displacement Image Alignment

Mears, B., Sevilla-Lara, L., Learned-Miller, E.

In British Machine Vision Conference (BMVC) , BMVA Press, September 2013 (inproceedings)

Abstract
While region-based image alignment algorithms that use gradient descent can achieve sub-pixel accuracy when they converge, their convergence depends on the smoothness of the image intensity values. Image smoothness is often enforced through the use of multiscale approaches in which images are smoothed and downsampled. Yet, these approaches typically use fixed smoothing parameters which may be appropriate for some images but not for others. Even for a particular image, the optimal smoothing parameters may depend on the magnitude of the transformation. When the transformation is large, the image should be smoothed more than when the transformation is small. Further, with gradient-based approaches, the optimal smoothing parameters may change with each iteration as the algorithm proceeds towards convergence. We address convergence issues related to the choice of smoothing parameters by deriving a Gauss-Newton gradient descent algorithm based on distribution fields (DFs) and proposing a method to dynamically select smoothing parameters at each iteration. DF and DF-like representations have previously been used in the context of tracking. In this work we incorporate DFs into a full affine model for region-based alignment and simultaneously search over parameterized sets of geometric and photometric transforms. We use a probabilistic interpretation of DFs to select smoothing parameters at each step in the optimization and show that this results in improved convergence rates.

pdf code [BibTex]

pdf code [BibTex]


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Metric Regression Forests for Human Pose Estimation

(Best Science Paper Award)

Pons-Moll, G., Taylor, J., Shotton, J., Hertzmann, A., Fitzgibbon, A.

In British Machine Vision Conference (BMVC) , BMVA Press, September 2013 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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Vision meets Robotics: The KITTI Dataset

Geiger, A., Lenz, P., Stiller, C., Urtasun, R.

International Journal of Robotics Research, 32(11):1231 - 1237 , Sage Publishing, September 2013 (article)

Abstract
We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS/IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations and range from freeways over rural areas to inner-city scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide.

pdf DOI [BibTex]

pdf DOI [BibTex]


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Human Pose Calculation from Optical Flow Data

Black, M., Loper, M., Romero, J., Zuffi, S.

European Patent Application EP 2843621 , August 2013 (patent)

Google Patents [BibTex]

Google Patents [BibTex]


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Statistics on Manifolds with Applications to Modeling Shape Deformations

Freifeld, O.

Brown University, August 2013 (phdthesis)

Abstract
Statistical models of non-rigid deformable shape have wide application in many fi elds, including computer vision, computer graphics, and biometry. We show that shape deformations are well represented through nonlinear manifolds that are also matrix Lie groups. These pattern-theoretic representations lead to several advantages over other alternatives, including a principled measure of shape dissimilarity and a natural way to compose deformations. Moreover, they enable building models using statistics on manifolds. Consequently, such models are superior to those based on Euclidean representations. We demonstrate this by modeling 2D and 3D human body shape. Shape deformations are only one example of manifold-valued data. More generally, in many computer-vision and machine-learning problems, nonlinear manifold representations arise naturally and provide a powerful alternative to Euclidean representations. Statistics is traditionally concerned with data in a Euclidean space, relying on the linear structure and the distances associated with such a space; this renders it inappropriate for nonlinear spaces. Statistics can, however, be generalized to nonlinear manifolds. Moreover, by respecting the underlying geometry, the statistical models result in not only more e ffective analysis but also consistent synthesis. We go beyond previous work on statistics on manifolds by showing how, even on these curved spaces, problems related to modeling a class from scarce data can be dealt with by leveraging information from related classes residing in di fferent regions of the space. We show the usefulness of our approach with 3D shape deformations. To summarize our main contributions: 1) We de fine a new 2D articulated model -- more expressive than traditional ones -- of deformable human shape that factors body-shape, pose, and camera variations. Its high realism is obtained from training data generated from a detailed 3D model. 2) We defi ne a new manifold-based representation of 3D shape deformations that yields statistical deformable-template models that are better than the current state-of-the- art. 3) We generalize a transfer learning idea from Euclidean spaces to Riemannian manifolds. This work demonstrates the value of modeling manifold-valued data and their statistics explicitly on the manifold. Specifi cally, the methods here provide new tools for shape analysis.

pdf Project Page [BibTex]


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D1.1 Specification of General Features of Scenarios and Robots for Benchmarking Through Competitions

Ahmad, A., Awaad, I., Amigoni, F., Berghofer, J., Bischoff, R., Bonarini, A., Dwiputra, R., Fontana, G., Hegger, F., Hochgeschwender, N., Iocchi, L., Kraetzschmar, G., Lima, P., Matteucci, M., Nardi, D., Schiaffonati, V., Schneider, S.

(FP7-ICT-601012 Revision 1.0), RoCKIn - Robot Competitions Kick Innovation in Cognitive Systems and Robotics, July 2013 (techreport)

Abstract
RoCKIn is a EU-funded project aiming to foster scientific progress and innovation in cognitive systems and robotics through the design and implementation of competitions. An additional objective of RoCKIn is to increase public awareness of the current state-of-the-art in robotics and the innovation potential of robotics applications. From these objectives several requirements for the work performed in RoCKIn can be derived: The RoCKIn competitions must start from convincing, easy-to-communicate user stories, that catch the attention of relevant stakeholders, the media, and the crowd. The user stories play the role of a mid- to long-term vision for a competition. Preferably, the user stories address economic, societal, or environmental problems. The RoCKIn competitions must pose open scientific challenges of interest to sufficiently many researchers to attract existing and new teams of robotics researchers for participation in the competition. The competitions need to promise some suitable reward, such as recognition in the scientific community, publicity for a team’s work, awards, or prize money, to justify the effort a team puts into the development of a competition entry. The competitions should be designed in such a way that they reward general, scientifically sound solutions to the challenge problems; such general solutions should score better than approaches that work only in narrowly defined contexts and are considred over-engineered. The challenges motivating the RoCKIn competitions must be broken down into suitable intermediate goals that can be reached with a limited team effort until the next competition and the project duration. The RoCKIn competitions must be well-defined and well-designed, with comprehensive rule books and instructions for the participants in order to guarantee a fair competition. The RoCKIn competitions must integrate competitions with benchmarking in order to provide comprehensive feedback for the teams about the suitability of particular functional modules, their overall architecture, and system integration. This document takes the first steps towards the RoCKIn goals. After outlining our approach, we present several user stories for further discussion within the community. The main objectives of this document are to identify and document relevant scenario features and the tasks and functionalities subject for benchmarking in the competitions.

[BibTex]

[BibTex]


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SocRob-MSL 2013 Team Description Paper for Middle Sized League

Messias, J., Ahmad, A., Reis, J., Serafim, M., Lima, P.

17th Annual RoboCup International Symposium 2013, July 2013 (techreport)

Abstract
This paper describes the status of the SocRob MSL robotic soccer team as required by the RoboCup 2013 qualification procedures. The team’s latest scientific and technical developments, since its last participation in RoboCup MSL, include further advances in cooperative perception; novel communication methods for distributed robotics; progressive deployment of the ROS middleware; improved localization through feature tracking and Mixture MCL; novel planning methods based on Petri nets and decision-theoretic frameworks; and hardware developments in ball-handling/kicking devices.

link (url) [BibTex]

link (url) [BibTex]