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2020


Grasping Field: Learning Implicit Representations for Human Grasps
Grasping Field: Learning Implicit Representations for Human Grasps

Karunratanakul, K., Yang, J., Zhang, Y., Black, M., Muandet, K., Tang, S.

In International Conference on 3D Vision (3DV), November 2020 (inproceedings)

Abstract
Robotic grasping of house-hold objects has made remarkable progress in recent years. Yet, human grasps are still difficult to synthesize realistically. There are several key reasons: (1) the human hand has many degrees of freedom (more than robotic manipulators); (2) the synthesized hand should conform to the surface of the object; and (3) it should interact with the object in a semantically and physically plausible manner. To make progress in this direction, we draw inspiration from the recent progress on learning-based implicit representations for 3D object reconstruction. Specifically, we propose an expressive representation for human grasp modelling that is efficient and easy to integrate with deep neural networks. Our insight is that every point in a three-dimensional space can be characterized by the signed distances to the surface of the hand and the object, respectively. Consequently, the hand, the object, and the contact area can be represented by implicit surfaces in a common space, in which the proximity between the hand and the object can be modelled explicitly. We name this 3D to 2D mapping as Grasping Field, parameterize it with a deep neural network, and learn it from data. We demonstrate that the proposed grasping field is an effective and expressive representation for human grasp generation. Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud. The extensive experiments demonstrate that our generative model compares favorably with a strong baseline and approaches the level of natural human grasps. Furthermore, based on the grasping field representation, we propose a deep network for the challenging task of 3D hand-object interaction reconstruction from a single RGB image. Our method improves the physical plausibility of the hand-object contact reconstruction and achieves comparable performance for 3D hand reconstruction compared to state-of-the-art methods. Our model and code are available for research purpose at https://github.com/korrawe/grasping_field.

pdf arXiv code [BibTex]


{GIF}: Generative Interpretable Faces
GIF: Generative Interpretable Faces

Ghosh, P., Gupta, P. S., Uziel, R., Ranjan, A., Black, M. J., Bolkart, T.

In International Conference on 3D Vision (3DV), November 2020 (inproceedings)

Abstract
Photo-realistic visualization and animation of expressive human faces have been a long standing challenge. 3D face modeling methods provide parametric control but generates unrealistic images, on the other hand, generative 2D models like GANs (Generative Adversarial Networks) output photo-realistic face images, but lack explicit control. Recent methods gain partial control, either by attempting to disentangle different factors in an unsupervised manner, or by adding control post hoc to a pre-trained model. Unconditional GANs, however, may entangle factors that are hard to undo later. We condition our generative model on pre-defined control parameters to encourage disentanglement in the generation process. Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model. While conditioning on FLAME parameters yields unsatisfactory results, we find that conditioning on rendered FLAME geometry and photometric details works well. This gives us a generative 2D face model named GIF (Generative Interpretable Faces) that offers FLAME's parametric control. Here, interpretable refers to the semantic meaning of different parameters. Given FLAME parameters for shape, pose, expressions, parameters for appearance, lighting, and an additional style vector, GIF outputs photo-realistic face images. We perform an AMT based perceptual study to quantitatively and qualitatively evaluate how well GIF follows its conditioning. The code, data, and trained model are publicly available for research purposes at http://gif.is.tue.mpg.de

pdf project code [BibTex]

pdf project code [BibTex]


{PLACE}: Proximity Learning of Articulation and Contact in {3D} Environments
PLACE: Proximity Learning of Articulation and Contact in 3D Environments

Zhang, S., Zhang, Y., Ma, Q., Black, M. J., Tang, S.

In International Conference on 3D Vision (3DV), November 2020 (inproceedings)

Abstract
High fidelity digital 3D environments have been proposed in recent years, however, it remains extremely challenging to automatically equip such environment with realistic human bodies. Existing work utilizes images, depth or semantic maps to represent the scene, and parametric human models to represent 3D bodies. While being straight-forward, their generated human-scene interactions often lack of naturalness and physical plausibility. Our key observation is that humans interact with the world through body-scene contact. To synthesize realistic human-scene interactions, it is essential to effectively represent the physical contact and proximity between the body and the world. To that end, we propose a novel interaction generation method, named PLACE(Proximity Learning of Articulation and Contact in 3D Environments), which explicitly models the proximity between the human body and the 3D scene around it. Specifically, given a set of basis points on a scene mesh, we leverage a conditional variational autoencoder to synthesize the minimum distances from the basis points to the human body surface. The generated proximal relationship exhibits which region of the scene is in contact with the person. Furthermore, based on such synthesized proximity, we are able to effectively obtain expressive 3D human bodies that interact with the 3D scene naturally. Our perceptual study shows that PLACE significantly improves the state-of-the-art method, approaching the realism of real human-scene interaction. We believe our method makes an important step towards the fully automatic synthesis of realistic 3D human bodies in 3D scenes. The code and model are available for research at https://sanweiliti.github.io/PLACE/PLACE.html

pdf arXiv project code [BibTex]

pdf arXiv project code [BibTex]


Learning a statistical full spine model from partial observations
Learning a statistical full spine model from partial observations

Meng, D., Keller, M., Boyer, E., Black, M., Pujades, S.

In Shape in Medical Imaging, pages: 122,133, (Editors: Reuter, Martin and Wachinger, Christian and Lombaert, Hervé and Paniagua, Beatriz and Goksel, Orcun and Rekik, Islem), Springer International Publishing, October 2020 (inproceedings)

Abstract
The study of the morphology of the human spine has attracted research attention for its many potential applications, such as image segmentation, bio-mechanics or pathology detection. However, as of today there is no publicly available statistical model of the 3D surface of the full spine. This is mainly due to the lack of openly available 3D data where the full spine is imaged and segmented. In this paper we propose to learn a statistical surface model of the full-spine (7 cervical, 12 thoracic and 5 lumbar vertebrae) from partial and incomplete views of the spine. In order to deal with the partial observations we use probabilistic principal component analysis (PPCA) to learn a surface shape model of the full spine. Quantitative evaluation demonstrates that the obtained model faithfully captures the shape of the population in a low dimensional space and generalizes to left out data. Furthermore, we show that the model faithfully captures the global correlations among the vertebrae shape. Given a partial observation of the spine, i.e. a few vertebrae, the model can predict the shape of unseen vertebrae with a mean error under 3 mm. The full-spine statistical model is trained on the VerSe 2019 public dataset and is publicly made available to the community for non-commercial purposes. (https://gitlab.inria.fr/spine/spine_model)

Gitlab Code PDF DOI [BibTex]

Gitlab Code PDF DOI [BibTex]


STAR: Sparse Trained Articulated Human Body Regressor
STAR: Sparse Trained Articulated Human Body Regressor

Osman, A. A. A., Bolkart, T., Black, M. J.

In European Conference on Computer Vision (ECCV) , August 2020 (inproceedings)

Abstract
The SMPL body model is widely used for the estimation, synthesis, and analysis of 3D human pose and shape. While popular, we show that SMPL has several limitations and introduce STAR, which is quantitatively and qualitatively superior to SMPL. First, SMPL has a huge number of parameters resulting from its use of global blend shapes. These dense pose-corrective offsets relate every vertex on the mesh to all the joints in the kinematic tree, capturing spurious long-range correlations. To address this, we define per-joint pose correctives and learn the subset of mesh vertices that are influenced by each joint movement. This sparse formulation results in more realistic deformations and significantly reduces the number of model parameters to 20% of SMPL. When trained on the same data as SMPL, STAR generalizes better despite having many fewer parameters. Second, SMPL factors pose-dependent deformations from body shape while, in reality, people with different shapes deform differently. Consequently, we learn shape-dependent pose-corrective blend shapes that depend on both body pose and BMI. Third, we show that the shape space of SMPL is not rich enough to capture the variation in the human population. We address this by training STAR with an additional 10,000 scans of male and female subjects, and show that this results in better model generalization. STAR is compact, generalizes better to new bodies and is a drop-in replacement for SMPL. STAR is publicly available for research purposes at http://star.is.tue.mpg.de.

Project Page Code Video paper supplemental [BibTex]


Monocular Expressive Body Regression through Body-Driven Attention
Monocular Expressive Body Regression through Body-Driven Attention

Choutas, V., Pavlakos, G., Bolkart, T., Tzionas, D., Black, M. J.

In Computer Vision – ECCV 2020, Springer International Publishing, Cham, August 2020 (inproceedings)

Abstract
To understand how people look, interact, or perform tasks,we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image. Most existing methods focus only on parts of the body. A few recent approaches reconstruct full expressive 3D humans from images using 3D body models that include the face and hands. These methods are optimization-based and thus slow, prone to local optima, and require 2D keypoints as input. We address these limitations by introducing ExPose (EXpressive POse and Shape rEgression), which directly regresses the body, face, and hands, in SMPL-X format, from an RGB image. This is a hard problem due to the high dimensionality of the body and the lack of expressive training data. Additionally, hands and faces are much smaller than the body, occupying very few image pixels. This makes hand and face estimation hard when body images are downscaled for neural networks. We make three main contributions. First, we account for the lack of training data by curating a dataset of SMPL-X fits on in-the-wild images. Second, we observe that body estimation localizes the face and hands reasonably well. We introduce body-driven attention for face and hand regions in the original image to extract higher-resolution crops that are fed to dedicated refinement modules. Third, these modules exploit part-specific knowledge from existing face and hand-only datasets. ExPose estimates expressive 3D humans more accurately than existing optimization methods at a small fraction of the computational cost. Our data, model and code are available for research at https://expose.is.tue.mpg.de.

code Short video Long video arxiv pdf suppl link (url) Project Page [BibTex]


GRAB: A Dataset of Whole-Body Human Grasping of Objects
GRAB: A Dataset of Whole-Body Human Grasping of Objects

Taheri, O., Ghorbani, N., Black, M. J., Tzionas, D.

In Computer Vision – ECCV 2020, Springer International Publishing, Cham, August 2020 (inproceedings)

Abstract
Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shape, and the 3D body motion over time. While "grasping" is commonly thought of as a single hand stably lifting an object, we capture the motion of the entire body and adopt the generalized notion of "whole-body grasps". Thus, we collect a new dataset, called GRAB (GRasping Actions with Bodies), of whole-body grasps, containing full 3D shape and pose sequences of 10 subjects interacting with 51 everyday objects of varying shape and size. Given MoCap markers, we fit the full 3D body shape and pose, including the articulated face and hands, as well as the 3D object pose. This gives detailed 3D meshes over time, from which we compute contact between the body and object. This is a unique dataset, that goes well beyond existing ones for modeling and understanding how humans grasp and manipulate objects, how their full body is involved, and how interaction varies with the task. We illustrate the practical value of GRAB with an example application; we train GrabNet, a conditional generative network, to predict 3D hand grasps for unseen 3D object shapes. The dataset and code are available for research purposes at https://grab.is.tue.mpg.de.

pdf suppl video (long) video (short) link (url) DOI [BibTex]

pdf suppl video (long) video (short) link (url) DOI [BibTex]


Learning to Dress 3D People in Generative Clothing
Learning to Dress 3D People in Generative Clothing

Ma, Q., Yang, J., Ranjan, A., Pujades, S., Pons-Moll, G., Tang, S., Black, M. J.

In Computer Vision and Pattern Recognition (CVPR), pages: 6468-6477, IEEE, June 2020 (inproceedings)

Abstract
Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not generalize to the complexity of dressed people in common images and videos. Additionally, current models lack the expressive power needed to represent the complex non-linear geometry of pose-dependent clothing shape. To address this, we learn a generative 3D mesh model of clothed people from 3D scans with varying pose and clothing. Specifically, we train a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making clothing an additional term on SMPL. Our model is conditioned on both pose and clothing type, giving the ability to draw samples of clothing to dress different body shapes in a variety of styles and poses. To preserve wrinkle detail, our Mesh-VAE-GAN extends patchwise discriminators to 3D meshes. Our model, named CAPE, represents global shape and fine local structure, effectively extending the SMPL body model to clothing. To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses.

Project page Code Short video Long video arXiv DOI [BibTex]

Project page Code Short video Long video arXiv DOI [BibTex]


{GENTEL : GENerating Training data Efficiently for Learning to segment medical images}
GENTEL : GENerating Training data Efficiently for Learning to segment medical images

Thakur, R. P., Rocamora, S. P., Goel, L., Pohmann, R., Machann, J., Black, M. J.

Congrès Reconnaissance des Formes, Image, Apprentissage et Perception (RFAIP), June 2020 (conference)

Abstract
Accurately segmenting MRI images is crucial for many clinical applications. However, manually segmenting images with accurate pixel precision is a tedious and time consuming task. In this paper we present a simple, yet effective method to improve the efficiency of the image segmentation process. We propose to transform the image annotation task into a binary choice task. We start by using classical image processing algorithms with different parameter values to generate multiple, different segmentation masks for each input MRI image. Then, instead of segmenting the pixels of the images, the user only needs to decide whether a segmentation is acceptable or not. This method allows us to efficiently obtain high quality segmentations with minor human intervention. With the selected segmentations, we train a state-of-the-art neural network model. For the evaluation, we use a second MRI dataset (1.5T Dataset), acquired with a different protocol and containing annotations. We show that the trained network i) is able to automatically segment cases where none of the classical methods obtain a high quality result ; ii) generalizes to the second MRI dataset, which was acquired with a different protocol and was never seen at training time ; and iii) enables detection of miss-annotations in this second dataset. Quantitatively, the trained network obtains very good results: DICE score - mean 0.98, median 0.99- and Hausdorff distance (in pixels) - mean 4.7, median 2.0-.

Project Page PDF [BibTex]

Project Page PDF [BibTex]


Generating 3D People in Scenes without People
Generating 3D People in Scenes without People

Zhang, Y., Hassan, M., Neumann, H., Black, M. J., Tang, S.

In Computer Vision and Pattern Recognition (CVPR), pages: 6194-6204, June 2020 (inproceedings)

Abstract
We present a fully automatic system that takes a 3D scene and generates plausible 3D human bodies that are posed naturally in that 3D scene. Given a 3D scene without people, humans can easily imagine how people could interact with the scene and the objects in it. However, this is a challenging task for a computer as solving it requires that (1) the generated human bodies to be semantically plausible within the 3D environment (e.g. people sitting on the sofa or cooking near the stove), and (2) the generated human-scene interaction to be physically feasible such that the human body and scene do not interpenetrate while, at the same time, body-scene contact supports physical interactions. To that end, we make use of the surface-based 3D human model SMPL-X. We first train a conditional variational autoencoder to predict semantically plausible 3D human poses conditioned on latent scene representations, then we further refine the generated 3D bodies using scene constraints to enforce feasible physical interaction. We show that our approach is able to synthesize realistic and expressive 3D human bodies that naturally interact with 3D environment. We perform extensive experiments demonstrating that our generative framework compares favorably with existing methods, both qualitatively and quantitatively. We believe that our scene-conditioned 3D human generation pipeline will be useful for numerous applications; e.g. to generate training data for human pose estimation, in video games and in VR/AR. Our project page for data and code can be seen at: \url{https://vlg.inf.ethz.ch/projects/PSI/}.

Code PDF DOI [BibTex]

Code PDF DOI [BibTex]


Learning Physics-guided Face Relighting under Directional Light
Learning Physics-guided Face Relighting under Directional Light

Nestmeyer, T., Lalonde, J., Matthews, I., Lehrmann, A. M.

In Conference on Computer Vision and Pattern Recognition, pages: 5123-5132, IEEE/CVF, June 2020 (inproceedings) Accepted

Abstract
Relighting is an essential step in realistically transferring objects from a captured image into another environment. For example, authentic telepresence in Augmented Reality requires faces to be displayed and relit consistent with the observer's scene lighting. We investigate end-to-end deep learning architectures that both de-light and relight an image of a human face. Our model decomposes the input image into intrinsic components according to a diffuse physics-based image formation model. We enable non-diffuse effects including cast shadows and specular highlights by predicting a residual correction to the diffuse render. To train and evaluate our model, we collected a portrait database of 21 subjects with various expressions and poses. Each sample is captured in a controlled light stage setup with 32 individual light sources. Our method creates precise and believable relighting results and generalizes to complex illumination conditions and challenging poses, including when the subject is not looking straight at the camera.

Paper [BibTex]

Paper [BibTex]


{VIBE}: Video Inference for Human Body Pose and Shape Estimation
VIBE: Video Inference for Human Body Pose and Shape Estimation

Kocabas, M., Athanasiou, N., Black, M. J.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 5252-5262, IEEE, June 2020 (inproceedings)

Abstract
Human motion is fundamental to understanding behavior. Despite progress on single-image 3D pose and shape estimation, existing video-based state-of-the-art methodsfail to produce accurate and natural motion sequences due to a lack of ground-truth 3D motion data for training. To address this problem, we propose “Video Inference for Body Pose and Shape Estimation” (VIBE), which makes use of an existing large-scale motion capture dataset (AMASS) together with unpaired, in-the-wild, 2D keypoint annotations. Our key novelty is an adversarial learning framework that leverages AMASS to discriminate between real human motions and those produced by our temporal pose and shape regression networks. We define a temporal network architecture and show that adversarial training, at the sequence level, produces kinematically plausible motion sequences without in-the-wild ground-truth 3D labels. We perform extensive experimentation to analyze the importance of motion and demonstrate the effectiveness of VIBE on challenging 3D pose estimation datasets, achieving state-of-the-art performance. Code and pretrained models are available at https://github.com/mkocabas/VIBE

arXiv code video supplemental video DOI Project Page [BibTex]

arXiv code video supplemental video DOI Project Page [BibTex]


From Variational to Deterministic Autoencoders
From Variational to Deterministic Autoencoders

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

8th International Conference on Learning Representations (ICLR) , April 2020, *equal contribution (conference)

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 link (url) [BibTex]

arXiv link (url) [BibTex]


Attractiveness and Confidence in Walking Style of Male and Female Virtual Characters
Attractiveness and Confidence in Walking Style of Male and Female Virtual Characters

Thaler, A., Bieg, A., Mahmood, N., Black, M. J., Mohler, B. J., Troje, N. F.

In IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), pages: 678-679, March 2020 (inproceedings)

Abstract
Animated virtual characters are essential to many applications. Little is known so far about biological and personality inferences made from a virtual character’s body shape and motion. Here, we investigated how sex-specific differences in walking style relate to the perceived attractiveness and confidence of male and female virtual characters. The characters were generated by reconstructing body shape and walking motion from optical motion capture data. The results suggest that sexual dimorphism in walking style plays a different role in attributing biological and personality traits to male and female virtual characters. This finding has important implications for virtual character animation.

pdf DOI [BibTex]

pdf DOI [BibTex]


Chained Representation Cycling: Learning to Estimate 3D Human Pose and Shape by Cycling Between Representations
Chained Representation Cycling: Learning to Estimate 3D Human Pose and Shape by Cycling Between Representations

Rueegg, N., Lassner, C., Black, M. J., Schindler, K.

In Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), pages: 5561-5569, Febuary 2020 (inproceedings)

Abstract
The goal of many computer vision systems is to transform image pixels into 3D representations. Recent popular models use neural networks to regress directly from pixels to 3D object parameters. Such an approach works well when supervision is available, but in problems like human pose and shape estimation, it is difficult to obtain natural images with 3D ground truth. To go one step further, we propose a new architecture that facilitates unsupervised, or lightly supervised, learning. The idea is to break the problem into a series of transformations between increasingly abstract representations. Each step involves a cycle designed to be learnable without annotated training data, and the chain of cycles delivers the final solution. Specifically, we use 2D body part segments as an intermediate representation that contains enough information to be lifted to 3D, and at the same time is simple enough to be learned in an unsupervised way. We demonstrate the method by learning 3D human pose and shape from un-paired and un-annotated images. We also explore varying amounts of paired data and show that cycling greatly alleviates the need for paired data. While we present results for modeling humans, our formulation is general and can be applied to other vision problems.

pdf [BibTex]

pdf [BibTex]

2009


Ball Joints for Marker-less Human Motion Capture
Ball Joints for Marker-less Human Motion Capture

Pons-Moll, G., Rosenhahn, B.

In IEEE Workshop on Applications of Computer Vision (WACV),, December 2009 (inproceedings)

pdf [BibTex]

2009

pdf [BibTex]


no image
Background Subtraction Based on Rank Constraint for Point Trajectories

Ahmad, A., Del Bue, A., Lima, P.

In pages: 1-3, October 2009 (inproceedings)

Abstract
This work deals with a background subtraction algorithm for a fish-eye lens camera having 3 degrees of freedom, 2 in translation and 1 in rotation. The core assumption in this algorithm is that the background is considered to be composed of a dominant static plane in the world frame. The novelty lies in developing a rank-constraint based background subtraction for equidistant projection model, a property of the fish-eye lens. A detail simulation result is presented to support the hypotheses explained in this paper.

link (url) [BibTex]

link (url) [BibTex]


Parametric Modeling of the Beating Heart with Respiratory Motion Extracted from Magnetic Resonance Images
Parametric Modeling of the Beating Heart with Respiratory Motion Extracted from Magnetic Resonance Images

Pons-Moll, G., Crosas, C., Tadmor, G., MacLeod, R., Rosenhahn, B., Brooks, D.

In IEEE Computers in Cardiology (CINC), September 2009 (inproceedings)

[BibTex]

[BibTex]


Computer cursor control by motor cortical signals in humans with tetraplegia
Computer cursor control by motor cortical signals in humans with tetraplegia

Kim, S., Simeral, J. D., Hochberg, L. R., Donoghue, J. P., Black, M. J.

In 7th Asian Control Conference, ASCC09, pages: 988-993, Hong Kong, China, August 2009 (inproceedings)

pdf [BibTex]

pdf [BibTex]


no image
Classification of colon polyps in NBI endoscopy using vascularization features

Stehle, T., Auer, R., Gross, S., Behrens, A., Wulff, J., Aach, T., Winograd, R., Trautwein, C., Tischendorf, J.

In Medical Imaging 2009: Computer-Aided Diagnosis, 7260, (Editors: N. Karssemeijer and M. L. Giger), SPIE, February 2009 (inproceedings)

Abstract
The evolution of colon cancer starts with colon polyps. There are two different types of colon polyps, namely hyperplasias and adenomas. Hyperplasias are benign polyps which are known not to evolve into cancer and, therefore, do not need to be removed. By contrast, adenomas have a strong tendency to become malignant. Therefore, they have to be removed immediately via polypectomy. For this reason, a method to differentiate reliably adenomas from hyperplasias during a preventive medical endoscopy of the colon (colonoscopy) is highly desirable. A recent study has shown that it is possible to distinguish both types of polyps visually by means of their vascularization. Adenomas exhibit a large amount of blood vessel capillaries on their surface whereas hyperplasias show only few of them. In this paper, we show the feasibility of computer-based classification of colon polyps using vascularization features. The proposed classification algorithm consists of several steps: For the critical part of vessel segmentation, we implemented and compared two segmentation algorithms. After a skeletonization of the detected blood vessel candidates, we used the results as seed points for the Fast Marching algorithm which is used to segment the whole vessel lumen. Subsequently, features are computed from this segmentation which are then used to classify the polyps. In leave-one-out tests on our polyp database (56 polyps), we achieve a correct classification rate of approximately 90%.

DOI [BibTex]

DOI [BibTex]


{One-shot scanning using de bruijn spaced grids}
One-shot scanning using de bruijn spaced grids

Ulusoy, A., Calakli, F., Taubin, G.

In Computer Vision Workshops (ICCV Workshops), 2009 IEEE 12th International Conference on, pages: 1786-1792, IEEE, 2009 (inproceedings)

Abstract
In this paper we present a new one-shot method to reconstruct the shape of dynamic 3D objects and scenes based on active illumination. In common with other related prior-art methods, a static grid pattern is projected onto the scene, a video sequence of the illuminated scene is captured, a shape estimate is produced independently for each video frame, and the one-shot property is realized at the expense of space resolution. The main challenge in grid-based one-shot methods is to engineer the pattern and algorithms so that the correspondence between pattern grid points and their images can be established very fast and without uncertainty. We present an efficient one-shot method which exploits simple geometric constraints to solve the correspondence problem. We also introduce De Bruijn spaced grids, a novel grid pattern, and show with strong empirical data that the resulting scheme is much more robust compared to those based on uniform spaced grids.

pdf link (url) DOI [BibTex]

pdf link (url) DOI [BibTex]


Estimating human shape and pose from a single image
Estimating human shape and pose from a single image

Guan, P., Weiss, A., Balan, A., Black, M. J.

In Int. Conf. on Computer Vision, ICCV, pages: 1381-1388, 2009 (inproceedings)

Abstract
We describe a solution to the challenging problem of estimating human body shape from a single photograph or painting. Our approach computes shape and pose parameters of a 3D human body model directly from monocular image cues and advances the state of the art in several directions. First, given a user-supplied estimate of the subject's height and a few clicked points on the body we estimate an initial 3D articulated body pose and shape. Second, using this initial guess we generate a tri-map of regions inside, outside and on the boundary of the human, which is used to segment the image using graph cuts. Third, we learn a low-dimensional linear model of human shape in which variations due to height are concentrated along a single dimension, enabling height-constrained estimation of body shape. Fourth, we formulate the problem of parametric human shape from shading. We estimate the body pose, shape and reflectance as well as the scene lighting that produces a synthesized body that robustly matches the image evidence. Quantitative experiments demonstrate how smooth shading provides powerful constraints on human shape. We further demonstrate a novel application in which we extract 3D human models from archival photographs and paintings.

pdf video - mov 25MB video - mp4 10MB YouTube Project Page [BibTex]

pdf video - mov 25MB video - mp4 10MB YouTube Project Page [BibTex]


On feature combination for multiclass object classification
On feature combination for multiclass object classification

Gehler, P., Nowozin, S.

In Proceedings of the Twelfth IEEE International Conference on Computer Vision, pages: 221-228, 2009, oral presentation (inproceedings)

project page, code, data GoogleScholar pdf DOI [BibTex]

project page, code, data GoogleScholar pdf DOI [BibTex]


no image
Evaluating the potential of primary motor and premotor cortex for mutltidimensional neuroprosthetic control of complete reaching and grasping actions

Vargas-Irwin, C. E., Yadollahpour, P., Shakhnarovich, G., Black, M. J., Donoghue, J. P.

2009 Abstract Viewer and Itinerary Planner. Society for Neuroscience, Society for Neuroscience, 2009, Online (conference)

[BibTex]

[BibTex]


Segmentation, Ordering and Multi-object Tracking Using Graphical   Models
Segmentation, Ordering and Multi-object Tracking Using Graphical Models

Wang, C., Gorce, M. D. L., Paragios, N.

In IEEE International Conference on Computer Vision (ICCV), 2009 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Modeling and Evaluation of Human-to-Robot Mapping of Grasps
Modeling and Evaluation of Human-to-Robot Mapping of Grasps

Romero, J., Kjellström, H., Kragic, D.

In International Conference on Advanced Robotics (ICAR), pages: 1-6, 2009 (inproceedings)

Pdf [BibTex]

Pdf [BibTex]


An additive latent feature model for transparent object recognition
An additive latent feature model for transparent object recognition

Fritz, M., Black, M., Bradski, G., Karayev, S., Darrell, T.

In Advances in Neural Information Processing Systems 22, NIPS, pages: 558-566, MIT Press, 2009 (inproceedings)

pdf slides [BibTex]

pdf slides [BibTex]


Let the kernel figure it out; Principled learning of pre-processing for kernel classifiers
Let the kernel figure it out; Principled learning of pre-processing for kernel classifiers

Gehler, P., Nowozin, S.

In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), pages: 2836-2843, IEEE Computer Society, 2009 (inproceedings)

doi project page pdf [BibTex]

doi project page pdf [BibTex]


Monocular Real-Time 3D Articulated Hand Pose Estimation
Monocular Real-Time 3D Articulated Hand Pose Estimation

Romero, J., Kjellström, H., Kragic, D.

In IEEE-RAS International Conference on Humanoid Robots, pages: 87-92, 2009 (inproceedings)

Pdf [BibTex]

Pdf [BibTex]


Grasp Recognition and Mapping on Humanoid Robots
Grasp Recognition and Mapping on Humanoid Robots

Do, M., Romero, J., Kjellström, H., Azad, P., Asfour, T., Kragic, D., Dillmann, R.

In IEEE-RAS International Conference on Humanoid Robots, pages: 465-471, 2009 (inproceedings)

Pdf Video [BibTex]

Pdf Video [BibTex]


4D Cardiac Segmentation of the Epicardium and Left Ventricle
4D Cardiac Segmentation of the Epicardium and Left Ventricle

Pons-Moll, G., Tadmor, G., MacLeod, R. S., Rosenhahn, B., Brooks, D. H.

In World Congress of Medical Physics and Biomedical Engineering (WC), 2009 (inproceedings)

[BibTex]

[BibTex]


Geometric Potential Force for the Deformable Model
Geometric Potential Force for the Deformable Model

Si Yong Yeo, Xianghua Xie, Igor Sazonov, Perumal Nithiarasu

In The 20th British Machine Vision Conference, pages: 1-11, 2009 (inproceedings)

Abstract
We propose a new external force field for deformable models which can be conve- niently generalized to high dimensions. The external force field is based on hypothesized interactions between the relative geometries of the deformable model and image gradi- ents. The evolution of the deformable model is solved using the level set method. The dynamic interaction forces between the geometries can greatly improve the deformable model performance in acquiring complex geometries and highly concave boundaries, and in dealing with weak image edges. The new deformable model can handle arbi- trary cross-boundary initializations. Here, we show that the proposed method achieve significant improvements when compared against existing state-of-the-art techniques.

[BibTex]

[BibTex]


no image
Computational mechanisms for the recognition of time sequences of images in the visual cortex

Tan, C., Jhuang, H., Singer, J., Serre, T., Sheinberg, D., Poggio, T.

Society for Neuroscience, 2009 (conference)

pdf [BibTex]

pdf [BibTex]


Interactive Inverse Kinematics for Monocular Motion Estimation
Interactive Inverse Kinematics for Monocular Motion Estimation

Morten Engell-Norregaard, Soren Hauberg, Jerome Lapuyade, Kenny Erleben, Kim S. Pedersen

In The 6th Workshop on Virtual Reality Interaction and Physical Simulation (VRIPHYS), 2009 (inproceedings)

Conference site Paper site [BibTex]

Conference site Paper site [BibTex]


A Comprehensive Grasp Taxonomy
A Comprehensive Grasp Taxonomy

Feix, T., Pawlik, R., Schmiedmayer, H., Romero, J., Kragic, D.

In Robotics, Science and Systems: Workshop on Understanding the Human Hand for Advancing Robotic Manipulation, 2009 (inproceedings)

Pdf [BibTex]

Pdf [BibTex]


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Population coding of ground truth motion in natural scenes in the early visual system

Stanley, G., Black, M. J., Lewis, J., Desbordes, G., Jin, J., Alonso, J.

COSYNE, 2009 (conference)

[BibTex]

[BibTex]


Level Set Based Automatic Segmentation of Human Aorta
Level Set Based Automatic Segmentation of Human Aorta

Si Yong Yeo, Xianghua Xie, Igor Sazonov, Perumal Nithiarasu

In International Conference on Computational & Mathematical Biomedical Engineering, pages: 242-245, 2009 (inproceedings)

[BibTex]

[BibTex]


In Defense of Orthonormality Constraints for Nonrigid Structure from Motion
In Defense of Orthonormality Constraints for Nonrigid Structure from Motion

Akhter, I., Sheikh, Y., Khan, S.

In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on, pages: 2447-2453, 2009 (inproceedings)

Abstract
In factorization approaches to nonrigid structure from motion, the 3D shape of a deforming object is usually modeled as a linear combination of a small number of basis shapes. The original approach to simultaneously estimate the shape basis and nonrigid structure exploited orthonormality constraints for metric rectification. Recently, it has been asserted that structure recovery through orthonormality constraints alone is inherently ambiguous and cannot result in a unique solution. This assertion has been accepted as conventional wisdom and is the justification of many remedial heuristics in literature. Our key contribution is to prove that orthonormality constraints are in fact sufficient to recover the 3D structure from image observations alone. We characterize the true nature of the ambiguity in using orthonormality constraints for the shape basis and show that it has no impact on structure reconstruction. We conclude from our experimentation that the primary challenge in using shape basis for nonrigid structure from motion is the difficulty in the optimization problem rather than the ambiguity in orthonormality constraints.

pdf [BibTex]

pdf [BibTex]


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Dynamic distortion correction for endoscopy systems with exchangeable optics

Stehle, T., Hennes, M., Gross, S., Behrens, A., Wulff, J., Aach, T.

In Bildverarbeitung für die Medizin 2009, pages: 142-146, Springer Berlin Heidelberg, 2009 (inproceedings)

Abstract
Endoscopic images are strongly affected by lens distortion caused by the use of wide angle lenses. In case of endoscopy systems with exchangeable optics, e.g. in bladder endoscopy or sinus endoscopy, the camera sensor and the optics do not form a rigid system but they can be shifted and rotated with respect to each other during an examination. This flexibility has a major impact on the location of the distortion centre as it is moved along with the optics. In this paper, we describe an algorithm for the dynamic correction of lens distortion in cystoscopy which is based on a one time calibration. For the compensation, we combine a conventional static method for distortion correction with an algorithm to detect the position and the orientation of the elliptic field of view. This enables us to estimate the position of the distortion centre according to the relative movement of camera and optics. Therewith, a distortion correction for arbitrary rotation angles and shifts becomes possible without performing static calibrations for every possible combination of shifts and angles beforehand.

link (url) DOI [BibTex]

link (url) DOI [BibTex]


Three Dimensional Monocular Human Motion Analysis in End-Effector Space
Three Dimensional Monocular Human Motion Analysis in End-Effector Space

Soren Hauberg, Jerome Lapuyade, Morten Engell-Norregaard, Kenny Erleben, Kim S. Pedersen

In Energy Minimization Methods in Computer Vision and Pattern Recognition, 5681, pages: 235-248, Lecture Notes in Computer Science, (Editors: Cremers, Daniel and Boykov, Yuri and Blake, Andrew and Schmidt, Frank), Springer Berlin Heidelberg, 2009 (inproceedings)

Publishers site Paper site PDF [BibTex]

Publishers site Paper site PDF [BibTex]


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Decoding visual motion from correlated firing of thalamic neurons

Stanley, G. B., Black, M. J., Desbordes, G., Jin, J., Wang, Y., Alonso, J.

2009 Abstract Viewer and Itinerary Planner. Society for Neuroscience, Society for Neuroscience, 2009 (conference)

[BibTex]

[BibTex]


Segmentation of Human Upper Airway Using a Level Set Based Deformable Model
Segmentation of Human Upper Airway Using a Level Set Based Deformable Model

Si Yong Yeo, Xianghua Xie, Igor Sazonov, Perumal Nithiarasu

In The 13th Medical Image Understanding and Analysis, 2009 (inproceedings)

[BibTex]

[BibTex]

2006


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Finding directional movement representations in motor cortical neural populations using nonlinear manifold learning

WorKim, S., Simeral, J., Jenkins, O., Donoghue, J., Black, M.

World Congress on Medical Physics and Biomedical Engineering 2006, Seoul, Korea, August 2006 (conference)

[BibTex]

2006

[BibTex]


A non-parametric {Bayesian} approach to spike sorting
A non-parametric Bayesian approach to spike sorting

Wood, F., Goldwater, S., Black, M. J.

In International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pages: 1165-1169, New York, NY, August 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Predicting {3D} people from {2D} pictures
Predicting 3D people from 2D pictures

(Best Paper)

Sigal, L., Black, M. J.

In Proc. IV Conf. on Articulated Motion and DeformableObjects (AMDO), LNCS 4069, pages: 185-195, July 2006 (inproceedings)

Abstract
We propose a hierarchical process for inferring the 3D pose of a person from monocular images. First we infer a learned view-based 2D body model from a single image using non-parametric belief propagation. This approach integrates information from bottom-up body-part proposal processes and deals with self-occlusion to compute distributions over limb poses. Then, we exploit a learned Mixture of Experts model to infer a distribution of 3D poses conditioned on 2D poses. This approach is more general than recent work on inferring 3D pose directly from silhouettes since the 2D body model provides a richer representation that includes the 2D joint angles and the poses of limbs that may be unobserved in the silhouette. We demonstrate the method in a laboratory setting where we evaluate the accuracy of the 3D poses against ground truth data. We also estimate 3D body pose in a monocular image sequence. The resulting 3D estimates are sufficiently accurate to serve as proposals for the Bayesian inference of 3D human motion over time

pdf pdf from publisher Video [BibTex]

pdf pdf from publisher Video [BibTex]


Specular flow and the recovery of surface structure
Specular flow and the recovery of surface structure

Roth, S., Black, M.

In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, 2, pages: 1869-1876, New York, NY, June 2006 (inproceedings)

Abstract
In scenes containing specular objects, the image motion observed by a moving camera may be an intermixed combination of optical flow resulting from diffuse reflectance (diffuse flow) and specular reflection (specular flow). Here, with few assumptions, we formalize the notion of specular flow, show how it relates to the 3D structure of the world, and develop an algorithm for estimating scene structure from 2D image motion. Unlike previous work on isolated specular highlights we use two image frames and estimate the semi-dense flow arising from the specular reflections of textured scenes. We parametrically model the image motion of a quadratic surface patch viewed from a moving camera. The flow is modeled as a probabilistic mixture of diffuse and specular components and the 3D shape is recovered using an Expectation-Maximization algorithm. Rather than treating specular reflections as noise to be removed or ignored, we show that the specular flow provides additional constraints on scene geometry that improve estimation of 3D structure when compared with reconstruction from diffuse flow alone. We demonstrate this for a set of synthetic and real sequences of mixed specular-diffuse objects.

pdf [BibTex]

pdf [BibTex]


An adaptive appearance model approach for model-based articulated object tracking
An adaptive appearance model approach for model-based articulated object tracking

Balan, A., Black, M. J.

In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, 1, pages: 758-765, New York, NY, June 2006 (inproceedings)

Abstract
The detection and tracking of three-dimensional human body models has progressed rapidly but successful approaches typically rely on accurate foreground silhouettes obtained using background segmentation. There are many practical applications where such information is imprecise. Here we develop a new image likelihood function based on the visual appearance of the subject being tracked. We propose a robust, adaptive, appearance model based on the Wandering-Stable-Lost framework extended to the case of articulated body parts. The method models appearance using a mixture model that includes an adaptive template, frame-to-frame matching and an outlier process. We employ an annealed particle filtering algorithm for inference and take advantage of the 3D body model to predict self occlusion and improve pose estimation accuracy. Quantitative tracking results are presented for a walking sequence with a 180 degree turn, captured with four synchronized and calibrated cameras and containing significant appearance changes and self-occlusion in each view.

pdf [BibTex]

pdf [BibTex]


Measure locally, reason globally: Occlusion-sensitive articulated pose estimation
Measure locally, reason globally: Occlusion-sensitive articulated pose estimation

Sigal, L., Black, M. J.

In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, CVPR, 2, pages: 2041-2048, New York, NY, June 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


Statistical analysis of the non-stationarity of neural population codes
Statistical analysis of the non-stationarity of neural population codes

Kim, S., Wood, F., Fellows, M., Donoghue, J. P., Black, M. J.

In BioRob 2006, The first IEEE / RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, pages: 295-299, Pisa, Italy, Febuary 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]


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How to choose the covariance for Gaussian process regression independently of the basis

Franz, M., Gehler, P.

In Proceedings of the Workshop Gaussian Processes in Practice, 2006 (inproceedings)

pdf [BibTex]

pdf [BibTex]