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2020


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), 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.

arxiv project page [BibTex]

2020


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), 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 (1) the generated human bodies should be semantically plausible with the 3D environment, e.g. people sitting on the sofa or cooking near the stove; (2) the generated human-scene interaction should be physically feasible in the way 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 pose 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.

PDF link (url) [BibTex]

PDF link (url) [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, 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 Computer Vision and Pattern Recognition (CVPR), 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 [BibTex]

arXiv code [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) Accepted

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]

arXiv [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), 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]

2012


Coregistration: Supplemental Material
Coregistration: Supplemental Material

Hirshberg, D., Loper, M., Rachlin, E., Black, M. J.

(No. 4), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf [BibTex]

2012

pdf [BibTex]


Lie Bodies: A Manifold Representation of {3D} Human Shape
Lie Bodies: A Manifold Representation of 3D Human Shape

Freifeld, O., Black, M. J.

In European Conf. on Computer Vision (ECCV), pages: 1-14, Part I, LNCS 7572, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

Abstract
Three-dimensional object shape is commonly represented in terms of deformations of a triangular mesh from an exemplar shape. Existing models, however, are based on a Euclidean representation of shape deformations. In contrast, we argue that shape has a manifold structure: For example, summing the shape deformations for two people does not necessarily yield a deformation corresponding to a valid human shape, nor does the Euclidean difference of these two deformations provide a meaningful measure of shape dissimilarity. Consequently, we define a novel manifold for shape representation, with emphasis on body shapes, using a new Lie group of deformations. This has several advantages. First we define triangle deformations exactly, removing non-physical deformations and redundant degrees of freedom common to previous methods. Second, the Riemannian structure of Lie Bodies enables a more meaningful definition of body shape similarity by measuring distance between bodies on the manifold of body shape deformations. Third, the group structure allows the valid composition of deformations. This is important for models that factor body shape deformations into multiple causes or represent shape as a linear combination of basis shapes. Finally, body shape variation is modeled using statistics on manifolds. Instead of modeling Euclidean shape variation with Principal Component Analysis we capture shape variation on the manifold using Principal Geodesic Analysis. Our experiments show consistent visual and quantitative advantages of Lie Bodies over traditional Euclidean models of shape deformation and our representation can be easily incorporated into existing methods.

pdf supplemental material youtube poster eigenshape video code Project Page Project Page Project Page [BibTex]

pdf supplemental material youtube poster eigenshape video code Project Page Project Page Project Page [BibTex]


Coregistration: Simultaneous alignment and modeling of articulated {3D} shape
Coregistration: Simultaneous alignment and modeling of articulated 3D shape

Hirshberg, D., Loper, M., Rachlin, E., Black, M.

In European Conf. on Computer Vision (ECCV), pages: 242-255, LNCS 7577, Part IV, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

Abstract
Three-dimensional (3D) shape models are powerful because they enable the inference of object shape from incomplete, noisy, or ambiguous 2D or 3D data. For example, realistic parameterized 3D human body models have been used to infer the shape and pose of people from images. To train such models, a corpus of 3D body scans is typically brought into registration by aligning a common 3D human-shaped template to each scan. This is an ill-posed problem that typically involves solving an optimization problem with regularization terms that penalize implausible deformations of the template. When aligning a corpus, however, we can do better than generic regularization. If we have a model of how the template can deform then alignments can be regularized by this model. Constructing a model of deformations, however, requires having a corpus that is already registered. We address this chicken-and-egg problem by approaching modeling and registration together. By minimizing a single objective function, we reliably obtain high quality registration of noisy, incomplete, laser scans, while simultaneously learning a highly realistic articulated body model. The model greatly improves robustness to noise and missing data. Since the model explains a corpus of body scans, it captures how body shape varies across people and poses.

pdf publisher site poster supplemental material (400MB) Project Page Project Page [BibTex]

pdf publisher site poster supplemental material (400MB) Project Page Project Page [BibTex]


Lie Bodies: A Manifold Representation of {3D} Human Shape. Supplemental Material
Lie Bodies: A Manifold Representation of 3D Human Shape. Supplemental Material

Freifeld, O., Black, M. J.

(No. 5), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf Project Page [BibTex]

pdf Project Page [BibTex]


MPI-Sintel Optical Flow Benchmark: Supplemental Material
MPI-Sintel Optical Flow Benchmark: Supplemental Material

Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J.

(No. 6), Max Planck Institute for Intelligent Systems, October 2012 (techreport)

pdf Project Page [BibTex]

pdf Project Page [BibTex]


Lessons and insights from creating a synthetic optical flow benchmark
Lessons and insights from creating a synthetic optical flow benchmark

Wulff, J., Butler, D. J., Stanley, G. B., Black, M. J.

In ECCV Workshop on Unsolved Problems in Optical Flow and Stereo Estimation, pages: 168-177, Part II, LNCS 7584, (Editors: A. Fusiello et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

pdf dataset poster youtube Project Page [BibTex]

pdf dataset poster youtube Project Page [BibTex]


3D2PM {--} 3D Deformable Part Models
3D2PM – 3D Deformable Part Models

Pepik, B., Gehler, P., Stark, M., Schiele, B.

In Proceedings of the European Conference on Computer Vision (ECCV), pages: 356-370, Lecture Notes in Computer Science, (Editors: Fitzgibbon, Andrew W. and Lazebnik, Svetlana and Perona, Pietro and Sato, Yoichi and Schmid, Cordelia), Springer, Firenze, October 2012 (inproceedings)

pdf video poster Project Page [BibTex]

pdf video poster Project Page [BibTex]


A naturalistic open source movie for optical flow evaluation
A naturalistic open source movie for optical flow evaluation

Butler, D. J., Wulff, J., Stanley, G. B., Black, M. J.

In European Conf. on Computer Vision (ECCV), pages: 611-625, Part IV, LNCS 7577, (Editors: A. Fitzgibbon et al. (Eds.)), Springer-Verlag, October 2012 (inproceedings)

Abstract
Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the image- and flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available.

pdf dataset youtube talk supplemental material Project Page Project Page [BibTex]

pdf dataset youtube talk supplemental material Project Page Project Page [BibTex]


A framework for relating neural activity to freely moving behavior
A framework for relating neural activity to freely moving behavior

Foster, J. D., Nuyujukian, P., Freifeld, O., Ryu, S., Black, M. J., Shenoy, K. V.

In 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC’12), pages: 2736 -2739 , IEEE, San Diego, August 2012 (inproceedings)

pdf Project Page [BibTex]

pdf Project Page [BibTex]


Pottics {--} The Potts Topic Model for Semantic Image Segmentation
Pottics – The Potts Topic Model for Semantic Image Segmentation

Dann, C., Gehler, P., Roth, S., Nowozin, S.

In Proceedings of 34th DAGM Symposium, pages: 397-407, Lecture Notes in Computer Science, (Editors: Pinz, Axel and Pock, Thomas and Bischof, Horst and Leberl, Franz), Springer, August 2012 (inproceedings)

code pdf poster [BibTex]

code pdf poster [BibTex]


Quasi-Newton Methods: A New Direction
Quasi-Newton Methods: A New Direction

Hennig, P., Kiefel, M.

In Proceedings of the 29th International Conference on Machine Learning, pages: 25-32, ICML ’12, (Editors: John Langford and Joelle Pineau), Omnipress, New York, NY, USA, July 2012 (inproceedings)

Abstract
Four decades after their invention, quasi- Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors.

website+code pdf link (url) [BibTex]

website+code pdf link (url) [BibTex]


From pictorial structures to deformable structures
From pictorial structures to deformable structures

Zuffi, S., Freifeld, O., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 3546-3553, IEEE, June 2012 (inproceedings)

Abstract
Pictorial Structures (PS) define a probabilistic model of 2D articulated objects in images. Typical PS models assume an object can be represented by a set of rigid parts connected with pairwise constraints that define the prior probability of part configurations. These models are widely used to represent non-rigid articulated objects such as humans and animals despite the fact that such objects have parts that deform non-rigidly. Here we define a new Deformable Structures (DS) model that is a natural extension of previous PS models and that captures the non-rigid shape deformation of the parts. Each part in a DS model is represented by a low-dimensional shape deformation space and pairwise potentials between parts capture how the shape varies with pose and the shape of neighboring parts. A key advantage of such a model is that it more accurately models object boundaries. This enables image likelihood models that are more discriminative than previous PS likelihoods. This likelihood is learned using training imagery annotated using a DS “puppet.” We focus on a human DS model learned from 2D projections of a realistic 3D human body model and use it to infer human poses in images using a form of non-parametric belief propagation.

pdf sup mat code poster Project Page Project Page Project Page Project Page [BibTex]

pdf sup mat code poster Project Page Project Page Project Page Project Page [BibTex]


Teaching 3D Geometry to Deformable Part Models
Teaching 3D Geometry to Deformable Part Models

Pepik, B., Stark, M., Gehler, P., Schiele, B.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 3362 -3369, IEEE, Providence, RI, USA, June 2012, oral presentation (inproceedings)

pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


Real-time Facial Feature Detection using Conditional Regression Forests
Real-time Facial Feature Detection using Conditional Regression Forests

Dantone, M., Gall, J., Fanelli, G., van Gool, L.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 2578-2585, IEEE, Providence, RI, USA, 2012 (inproceedings)

code pdf Project Page [BibTex]

code pdf Project Page [BibTex]


Latent Hough Transform for Object Detection
Latent Hough Transform for Object Detection

Razavi, N., Gall, J., Kohli, P., van Gool, L.

In European Conference on Computer Vision (ECCV), 7574, pages: 312-325, LNCS, Springer, 2012 (inproceedings)

pdf Project Page [BibTex]

pdf Project Page [BibTex]


Destination Flow for Crowd Simulation
Destination Flow for Crowd Simulation

Pellegrini, S., Gall, J., Sigal, L., van Gool, L.

In Workshop on Analysis and Retrieval of Tracked Events and Motion in Imagery Streams, 7585, pages: 162-171, LNCS, Springer, 2012 (inproceedings)

pdf Project Page [BibTex]

pdf Project Page [BibTex]


From Deformations to Parts: Motion-based Segmentation of {3D} Objects
From Deformations to Parts: Motion-based Segmentation of 3D Objects

Ghosh, S., Sudderth, E., Loper, M., Black, M.

In Advances in Neural Information Processing Systems 25 (NIPS), pages: 2006-2014, (Editors: P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger), MIT Press, 2012 (inproceedings)

Abstract
We develop a method for discovering the parts of an articulated object from aligned meshes of the object in various three-dimensional poses. We adapt the distance dependent Chinese restaurant process (ddCRP) to allow nonparametric discovery of a potentially unbounded number of parts, while simultaneously guaranteeing a spatially connected segmentation. To allow analysis of datasets in which object instances have varying 3D shapes, we model part variability across poses via affine transformations. By placing a matrix normal-inverse-Wishart prior on these affine transformations, we develop a ddCRP Gibbs sampler which tractably marginalizes over transformation uncertainty. Analyzing a dataset of humans captured in dozens of poses, we infer parts which provide quantitatively better deformation predictions than conventional clustering methods.

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

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


Interactive Object Detection
Interactive Object Detection

Yao, A., Gall, J., Leistner, C., van Gool, L.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 3242-3249, IEEE, Providence, RI, USA, 2012 (inproceedings)

video pdf Project Page [BibTex]

video pdf Project Page [BibTex]


Motion Capture of Hands in Action using Discriminative Salient Points
Motion Capture of Hands in Action using Discriminative Salient Points

Ballan, L., Taneja, A., Gall, J., van Gool, L., Pollefeys, M.

In European Conference on Computer Vision (ECCV), 7577, pages: 640-653, LNCS, Springer, 2012 (inproceedings)

data video pdf supplementary Project Page [BibTex]

data video pdf supplementary Project Page [BibTex]


Sparsity Potentials for Detecting Objects with the Hough Transform
Sparsity Potentials for Detecting Objects with the Hough Transform

Razavi, N., Alvar, N., Gall, J., van Gool, L.

In British Machine Vision Conference (BMVC), pages: 11.1-11.10, (Editors: Bowden, Richard and Collomosse, John and Mikolajczyk, Krystian), BMVA Press, 2012 (inproceedings)

pdf Project Page [BibTex]

pdf Project Page [BibTex]


Metric Learning from Poses for Temporal Clustering of Human Motion
Metric Learning from Poses for Temporal Clustering of Human Motion

L’opez-M’endez, A., Gall, J., Casas, J., van Gool, L.

In British Machine Vision Conference (BMVC), pages: 49.1-49.12, (Editors: Bowden, Richard and Collomosse, John and Mikolajczyk, Krystian), BMVA Press, 2012 (inproceedings)

video pdf Project Page Project Page [BibTex]

video pdf Project Page Project Page [BibTex]


Local Context Priors for Object Proposal Generation
Local Context Priors for Object Proposal Generation

Ristin, M., Gall, J., van Gool, L.

In Asian Conference on Computer Vision (ACCV), 7724, pages: 57-70, LNCS, Springer-Verlag, 2012 (inproceedings)

pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


Layered segmentation and optical flow estimation over time
Layered segmentation and optical flow estimation over time

Sun, D., Sudderth, E., Black, M. J.

In IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages: 1768-1775, IEEE, 2012 (inproceedings)

Abstract
Layered models provide a compelling approach for estimating image motion and segmenting moving scenes. Previous methods, however, have failed to capture the structure of complex scenes, provide precise object boundaries, effectively estimate the number of layers in a scene, or robustly determine the depth order of the layers. Furthermore, previous methods have focused on optical flow between pairs of frames rather than longer sequences. We show that image sequences with more frames are needed to resolve ambiguities in depth ordering at occlusion boundaries; temporal layer constancy makes this feasible. Our generative model of image sequences is rich but difficult to optimize with traditional gradient descent methods. We propose a novel discrete approximation of the continuous objective in terms of a sequence of depth-ordered MRFs and extend graph-cut optimization methods with new “moves” that make joint layer segmentation and motion estimation feasible. Our optimizer, which mixes discrete and continuous optimization, automatically determines the number of layers and reasons about their depth ordering. We demonstrate the value of layered models, our optimization strategy, and the use of more than two frames on both the Middlebury optical flow benchmark and the MIT layer segmentation benchmark.

pdf sup mat poster Project Page Project Page [BibTex]

pdf sup mat poster Project Page Project Page [BibTex]


Spatial Measures between Human Poses for Classification and Understanding
Spatial Measures between Human Poses for Classification and Understanding

Soren Hauberg, Kim S. Pedersen

In Articulated Motion and Deformable Objects, 7378, pages: 26-36, LNCS, (Editors: Perales, Francisco J. and Fisher, Robert B. and Moeslund, Thomas B.), Springer Berlin Heidelberg, 2012 (inproceedings)

Publishers site Project Page [BibTex]

Publishers site Project Page [BibTex]


A Geometric Take on Metric Learning
A Geometric Take on Metric Learning

Hauberg, S., Freifeld, O., Black, M. J.

In Advances in Neural Information Processing Systems (NIPS) 25, pages: 2033-2041, (Editors: P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger), MIT Press, 2012 (inproceedings)

Abstract
Multi-metric learning techniques learn local metric tensors in different parts of a feature space. With such an approach, even simple classifiers can be competitive with the state-of-the-art because the distance measure locally adapts to the structure of the data. The learned distance measure is, however, non-metric, which has prevented multi-metric learning from generalizing to tasks such as dimensionality reduction and regression in a principled way. We prove that, with appropriate changes, multi-metric learning corresponds to learning the structure of a Riemannian manifold. We then show that this structure gives us a principled way to perform dimensionality reduction and regression according to the learned metrics. Algorithmically, we provide the first practical algorithm for computing geodesics according to the learned metrics, as well as algorithms for computing exponential and logarithmic maps on the Riemannian manifold. Together, these tools let many Euclidean algorithms take advantage of multi-metric learning. We illustrate the approach on regression and dimensionality reduction tasks that involve predicting measurements of the human body from shape data.

PDF Youtube Suppl. material Poster Project Page [BibTex]

PDF Youtube Suppl. material Poster Project Page [BibTex]

2011


Home {3D} body scans from noisy image and range data
Home 3D body scans from noisy image and range data

Weiss, A., Hirshberg, D., Black, M.

In Int. Conf. on Computer Vision (ICCV), pages: 1951-1958, IEEE, Barcelona, November 2011 (inproceedings)

Abstract
The 3D shape of the human body is useful for applications in fitness, games and apparel. Accurate body scanners, however, are expensive, limiting the availability of 3D body models. We present a method for human shape reconstruction from noisy monocular image and range data using a single inexpensive commodity sensor. The approach combines low-resolution image silhouettes with coarse range data to estimate a parametric model of the body. Accurate 3D shape estimates are obtained by combining multiple monocular views of a person moving in front of the sensor. To cope with varying body pose, we use a SCAPE body model which factors 3D body shape and pose variations. This enables the estimation of a single consistent shape while allowing pose to vary. Additionally, we describe a novel method to minimize the distance between the projected 3D body contour and the image silhouette that uses analytic derivatives of the objective function. We propose a simple method to estimate standard body measurements from the recovered SCAPE model and show that the accuracy of our method is competitive with commercial body scanning systems costing orders of magnitude more.

pdf YouTube poster Project Page Project Page [BibTex]

2011

pdf YouTube poster Project Page Project Page [BibTex]


Evaluating the Automated Alignment of {3D} Human Body Scans
Evaluating the Automated Alignment of 3D Human Body Scans

Hirshberg, D. A., Loper, M., Rachlin, E., Tsoli, A., Weiss, A., Corner, B., Black, M. J.

In 2nd International Conference on 3D Body Scanning Technologies, pages: 76-86, (Editors: D’Apuzzo, Nicola), Hometrica Consulting, Lugano, Switzerland, October 2011 (inproceedings)

Abstract
The statistical analysis of large corpora of human body scans requires that these scans be in alignment, either for a small set of key landmarks or densely for all the vertices in the scan. Existing techniques tend to rely on hand-placed landmarks or algorithms that extract landmarks from scans. The former is time consuming and subjective while the latter is error prone. Here we show that a model-based approach can align meshes automatically, producing alignment accuracy similar to that of previous methods that rely on many landmarks. Specifically, we align a low-resolution, artist-created template body mesh to many high-resolution laser scans. Our alignment procedure employs a robust iterative closest point method with a regularization that promotes smooth and locally rigid deformation of the template mesh. We evaluate our approach on 50 female body models from the CAESAR dataset that vary significantly in body shape. To make the method fully automatic, we define simple feature detectors for the head and ankles, which provide initial landmark locations. We find that, if body poses are fairly similar, as in CAESAR, the fully automated method provides dense alignments that enable statistical analysis and anthropometric measurement.

pdf slides DOI Project Page [BibTex]

pdf slides DOI Project Page [BibTex]


Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance
Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance

Gehler, P., Rother, C., Kiefel, M., Zhang, L., Schölkopf, B.

In Advances in Neural Information Processing Systems 24, pages: 765-773, (Editors: Shawe-Taylor, John and Zemel, Richard S. and Bartlett, Peter L. and Pereira, Fernando C. N. and Weinberger, Kilian Q.), Curran Associates, Inc., Red Hook, NY, USA, 2011 (inproceedings)

Abstract
We address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem. We take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values. We show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally, we are able to improve on state-of-the-art results by integrating edge information into our model. We believe that our new approach is an excellent starting point for future developments in this field.

website + code pdf poster Project Page Project Page [BibTex]

website + code pdf poster Project Page Project Page [BibTex]


Combining wireless neural recording and video capture for the analysis of natural gait
Combining wireless neural recording and video capture for the analysis of natural gait

Foster, J., Freifeld, O., Nuyujukian, P., Ryu, S., Black, M. J., Shenoy, K.

In Proc. 5th Int. IEEE EMBS Conf. on Neural Engineering, pages: 613-616, IEEE, 2011 (inproceedings)

pdf Project Page [BibTex]

pdf Project Page [BibTex]


Shape and pose-invariant correspondences using probabilistic geodesic surface embedding
Shape and pose-invariant correspondences using probabilistic geodesic surface embedding

Tsoli, A., Black, M. J.

In 33rd Annual Symposium of the German Association for Pattern Recognition (DAGM), 6835, pages: 256-265, Lecture Notes in Computer Science, (Editors: Mester, Rudolf and Felsberg, Michael), Springer, 2011 (inproceedings)

Abstract
Correspondence between non-rigid deformable 3D objects provides a foundation for object matching and retrieval, recognition, and 3D alignment. Establishing 3D correspondence is challenging when there are non-rigid deformations or articulations between instances of a class. We present a method for automatically finding such correspondences that deals with significant variations in pose, shape and resolution between pairs of objects.We represent objects as triangular meshes and consider normalized geodesic distances as representing their intrinsic characteristics. Geodesic distances are invariant to pose variations and nearly invariant to shape variations when properly normalized. The proposed method registers two objects by optimizing a joint probabilistic model over a subset of vertex pairs between the objects. The model enforces preservation of geodesic distances between corresponding vertex pairs and inference is performed using loopy belief propagation in a hierarchical scheme. Additionally our method prefers solutions in which local shape information is consistent at matching vertices. We quantitatively evaluate our method and show that is is more accurate than a state of the art method.

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