Header logo is ps

14 results (View BibTeX file of all listed publications)

2017

Thumb xl flamewebteaserwide
Learning a model of facial shape and expression from 4D scans

Li, T., Bolkart, T., Black, M. J., Li, H., Romero, J.

ACM Transactions on Graphics, 36(6):194:1-194:17, November 2017, Two first authors contributed equally (article)

Abstract
The field of 3D face modeling has a large gap between high-end and low-end methods. At the high end, the best facial animation is indistinguishable from real humans, but this comes at the cost of extensive manual labor. At the low end, face capture from consumer depth sensors relies on 3D face models that are not expressive enough to capture the variability in natural facial shape and expression. We seek a middle ground by learning a facial model from thousands of accurately aligned 3D scans. Our FLAME model (Faces Learned with an Articulated Model and Expressions) is designed to work with existing graphics software and be easy to fit to data. FLAME uses a linear shape space trained from 3800 scans of human heads. FLAME combines this linear shape space with an articulated jaw, neck, and eyeballs, pose-dependent corrective blendshapes, and additional global expression from 4D face sequences in the D3DFACS dataset along with additional 4D sequences.We accurately register a template mesh to the scan sequences and make the D3DFACS registrations available for research purposes. In total the model is trained from over 33, 000 scans. FLAME is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model. We compare FLAME to these models by fitting them to static 3D scans and 4D sequences using the same optimization method. FLAME is significantly more accurate and is available for research purposes (http://flame.is.tue.mpg.de).

data/model video code chumpy code tensorflow paper supplemental Project Page [BibTex]

2017

Li, T., Bolkart, T., Black, M. J., Li, H., Romero, J. Learning a model of facial shape and expression from 4D scans ACM Transactions on Graphics, 36(6):194:1-194:17, November 2017, Two first authors contributed equally (article)

data/model video code chumpy code tensorflow paper supplemental Project Page [BibTex]

Thumb xl molbert
Investigating Body Image Disturbance in Anorexia Nervosa Using Novel Biometric Figure Rating Scales: A Pilot Study

Mölbert, S. C., Thaler, A., Streuber, S., Black, M. J., Karnath, H., Zipfel, S., Mohler, B., Giel, K. E.

European Eating Disorders Review, 25(6):607-612, November 2017 (article)

Abstract
This study uses novel biometric figure rating scales (FRS) spanning body mass index (BMI) 13.8 to 32.2 kg/m2 and BMI 18 to 42 kg/m2. The aims of the study were (i) to compare FRS body weight dissatisfaction and perceptual distortion of women with anorexia nervosa (AN) to a community sample; (ii) how FRS parameters are associated with questionnaire body dissatisfaction, eating disorder symptoms and appearance comparison habits; and (iii) whether the weight spectrum of the FRS matters. Women with AN (n = 24) and a community sample of women (n = 104) selected their current and ideal body on the FRS and completed additional questionnaires. Women with AN accurately picked the body that aligned best with their actual weight in both FRS. Controls underestimated their BMI in the FRS 14–32 and were accurate in the FRS 18–42. In both FRS, women with AN desired a body close to their actual BMI and controls desired a thinner body. Our observations suggest that body image disturbance in AN is unlikely to be characterized by a visual perceptual disturbance, but rather by an idealization of underweight in conjunction with high body dissatisfaction. The weight spectrum of FRS can influence the accuracy of BMI estimation.

publisher DOI Project Page [BibTex]

Thumb xl manoteaser
Embodied Hands: Modeling and Capturing Hands and Bodies Together

Romero, J., Tzionas, D., Black, M. J.

ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6):245:1-245:17, 245:1–245:17, ACM, November 2017 (article)

Abstract
Humans move their hands and bodies together to communicate and solve tasks. Capturing and replicating such coordinated activity is critical for virtual characters that behave realistically. Surprisingly, most methods treat the 3D modeling and tracking of bodies and hands separately. Here we formulate a model of hands and bodies interacting together and fit it to full-body 4D sequences. When scanning or capturing the full body in 3D, hands are small and often partially occluded, making their shape and pose hard to recover. To cope with low-resolution, occlusion, and noise, we develop a new model called MANO (hand Model with Articulated and Non-rigid defOrmations). MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses. The model is realistic, low-dimensional, captures non-rigid shape changes with pose, is compatible with standard graphics packages, and can fit any human hand. MANO provides a compact mapping from hand poses to pose blend shape corrections and a linear manifold of pose synergies. We attach MANO to a standard parameterized 3D body shape model (SMPL), resulting in a fully articulated body and hand model (SMPL+H). We illustrate SMPL+H by fitting complex, natural, activities of subjects captured with a 4D scanner. The fitting is fully automatic and results in full body models that move naturally with detailed hand motions and a realism not seen before in full body performance capture. The models and data are freely available for research purposes at http://mano.is.tue.mpg.de.

website youtube paper suppl video link (url) DOI Project Page [BibTex]

Romero, J., Tzionas, D., Black, M. J. Embodied Hands: Modeling and Capturing Hands and Bodies Together ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6):245:1-245:17, 245:1–245:17, ACM, November 2017 (article)

website youtube paper suppl video link (url) DOI Project Page [BibTex]

Thumb xl cover tro paper
An Online Scalable Approach to Unified Multirobot Cooperative Localization and Object Tracking

Ahmad, A., Lawless, G., Lima, P.

IEEE Transactions on Robotics (T-RO), 33, pages: 1184 - 1199, October 2017 (article)

Abstract
In this article we present a unified approach for multi-robot cooperative simultaneous localization and object tracking based on particle filters. Our approach is scalable with respect to the number of robots in the team. We introduce a method that reduces, from an exponential to a linear growth, the space and computation time requirements with respect to the number of robots in order to maintain a given level of accuracy in the full state estimation. Our method requires no increase in the number of particles with respect to the number of robots. However, in our method each particle represents a full state hypothesis, leading to the linear dependency on the number of robots of both space and time complexity. The derivation of the algorithm implementing our approach from a standard particle filter algorithm and its complexity analysis are presented. Through an extensive set of simulation experiments on a large number of randomized datasets, we demonstrate the correctness and efficacy of our approach. Through real robot experiments on a standardized open dataset of a team of four soccer playing robots tracking a ball, we evaluate our method's estimation accuracy with respect to the ground truth values. Through comparisons with other methods based on i) nonlinear least squares minimization and ii) joint extended Kalman filter, we further highlight our method's advantages. Finally, we also present a robustness test for our approach by evaluating it under scenarios of communication and vision failure in teammate robots.

Published Version link (url) DOI [BibTex]

Thumb xl early stopping teaser
Early Stopping Without a Validation Set

Mahsereci, M., Balles, L., Lassner, C., Hennig, P.

arXiv preprint arXiv:1703.09580, 2017 (article)

Abstract
Early stopping is a widely used technique to prevent poor generalization performance when training an over-expressive model by means of gradient-based optimization. To find a good point to halt the optimizer, a common practice is to split the dataset into a training and a smaller validation set to obtain an ongoing estimate of the generalization performance. In this paper we propose a novel early stopping criterion which is based on fast-to-compute, local statistics of the computed gradients and entirely removes the need for a held-out validation set. Our experiments show that this is a viable approach in the setting of least-squares and logistic regression as well as neural networks.

link (url) Project Page Project Page [BibTex]

Thumb xl web image
Data-Driven Physics for Human Soft Tissue Animation

Kim, M., Pons-Moll, G., Pujades, S., Bang, S., Kim, J., Black, M. J., Lee, S.

ACM Transactions on Graphics, (Proc. SIGGRAPH), 36(4):54:1-54:12, 2017 (article)

Abstract
Data driven models of human poses and soft-tissue deformations can produce very realistic results, but they only model the visible surface of the human body and cannot create skin deformation due to interactions with the environment. Physical simulations can generalize to external forces, but their parameters are difficult to control. In this paper, we present a layered volumetric human body model learned from data. Our model is composed of a data-driven inner layer and a physics-based external layer. The inner layer is driven with a volumetric statistical body model (VSMPL). The soft tissue layer consists of a tetrahedral mesh that is driven using the finite element method (FEM). Model parameters, namely the segmentation of the body into layers and the soft tissue elasticity, are learned directly from 4D registrations of humans exhibiting soft tissue deformations. The learned two layer model is a realistic full-body avatar that generalizes to novel motions and external forces. Experiments show that the resulting avatars produce realistic results on held out sequences and react to external forces. Moreover, the model supports the retargeting of physical properties from one avatar when they share the same topology.

video paper link (url) Project Page [BibTex]

Kim, M., Pons-Moll, G., Pujades, S., Bang, S., Kim, J., Black, M. J., Lee, S. Data-Driven Physics for Human Soft Tissue Animation ACM Transactions on Graphics, (Proc. SIGGRAPH), 36(4):54:1-54:12, 2017 (article)

video paper link (url) Project Page [BibTex]

Thumb xl web teaser eg
Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs

(Best Paper, Eurographics 2017)

Marcard, T. V., Rosenhahn, B., Black, M., Pons-Moll, G.

Computer Graphics Forum 36(2), Proceedings of the 38th Annual Conference of the European Association for Computer Graphics (Eurographics), pages: 349-360 , 2017 (article)

Abstract
We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under-constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables motion capture using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall

video pdf Project Page [BibTex]

Marcard, T. V., Rosenhahn, B., Black, M., Pons-Moll, G. Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs Computer Graphics Forum 36(2), Proceedings of the 38th Annual Conference of the European Association for Computer Graphics (Eurographics), pages: 349-360 , 2017 (article)

video pdf Project Page [BibTex]

Thumb xl pami 2017 teaser
Efficient 2D and 3D Facade Segmentation using Auto-Context

Gadde, R., Jampani, V., Marlet, R., Gehler, P.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 (article)

Abstract
This paper introduces a fast and efficient segmentation technique for 2D images and 3D point clouds of building facades. Facades of buildings are highly structured and consequently most methods that have been proposed for this problem aim to make use of this strong prior information. Contrary to most prior work, we are describing a system that is almost domain independent and consists of standard segmentation methods. We train a sequence of boosted decision trees using auto-context features. This is learned using stacked generalization. We find that this technique performs better, or comparable with all previous published methods and present empirical results on all available 2D and 3D facade benchmark datasets. The proposed method is simple to implement, easy to extend, and very efficient at test-time inference.

arXiv Project Page [BibTex]

Gadde, R., Jampani, V., Marlet, R., Gehler, P. Efficient 2D and 3D Facade Segmentation using Auto-Context IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 (article)

arXiv Project Page [BibTex]

Thumb xl web image
ClothCap: Seamless 4D Clothing Capture and Retargeting

Pons-Moll, G., Pujades, S., Hu, S., Black, M.

ACM Transactions on Graphics, (Proc. SIGGRAPH), 36(4):73:1-73:15, ACM, New York, NY, USA, 2017, Two first authors contributed equally (article)

Abstract
Designing and simulating realistic clothing is challenging and, while several methods have addressed the capture of clothing from 3D scans, previous methods have been limited to single garments and simple motions, lack detail, or require specialized texture patterns. Here we address the problem of capturing regular clothing on fully dressed people in motion. People typically wear multiple pieces of clothing at a time. To estimate the shape of such clothing, track it over time, and render it believably, each garment must be segmented from the others and the body. Our ClothCap approach uses a new multi-part 3D model of clothed bodies, automatically segments each piece of clothing, estimates the naked body shape and pose under the clothing, and tracks the 3D deformations of the clothing over time. We estimate the garments and their motion from 4D scans; that is, high-resolution 3D scans of the subject in motion at 60 fps. The model allows us to capture a clothed person in motion, extract their clothing, and retarget the clothing to new body shapes. ClothCap provides a step towards virtual try-on with a technology for capturing, modeling, and analyzing clothing in motion.

video project_page paper link (url) DOI Project Page Project Page [BibTex]

Pons-Moll, G., Pujades, S., Hu, S., Black, M. ClothCap: Seamless 4D Clothing Capture and Retargeting ACM Transactions on Graphics, (Proc. SIGGRAPH), 36(4):73:1-73:15, ACM, New York, NY, USA, 2017, Two first authors contributed equally (article)

video project_page paper link (url) DOI Project Page Project Page [BibTex]

2000

Thumb xl ijcv2000teaser
Probabilistic detection and tracking of motion boundaries

Black, M. J., Fleet, D. J.

Int. J. of Computer Vision, 38(3):231-245, July 2000 (article)

Abstract
We propose a Bayesian framework for representing and recognizing local image motion in terms of two basic models: translational motion and motion boundaries. Motion boundaries are represented using a non-linear generative model that explicitly encodes the orientation of the boundary, the velocities on either side, the motion of the occluding edge over time, and the appearance/disappearance of pixels at the boundary. We represent the posterior probability distribution over the model parameters given the image data using discrete samples. This distribution is propagated over time using a particle filtering algorithm. To efficiently represent such a high-dimensional space we initialize samples using the responses of a low-level motion discontinuity detector. The formulation and computational model provide a general probabilistic framework for motion estimation with multiple, non-linear, models.

pdf pdf from publisher Video [BibTex]

2000

Black, M. J., Fleet, D. J. Probabilistic detection and tracking of motion boundaries Int. J. of Computer Vision, 38(3):231-245, July 2000 (article)

pdf pdf from publisher Video [BibTex]

Thumb xl bildschirmfoto 2012 12 06 um 09.22.34
Design and use of linear models for image motion analysis

Fleet, D. J., Black, M. J., Yacoob, Y., Jepson, A. D.

Int. J. of Computer Vision, 36(3):171-193, 2000 (article)

Abstract
Linear parameterized models of optical flow, particularly affine models, have become widespread in image motion analysis. The linear model coefficients are straightforward to estimate, and they provide reliable estimates of the optical flow of smooth surfaces. Here we explore the use of parameterized motion models that represent much more varied and complex motions. Our goals are threefold: to construct linear bases for complex motion phenomena; to estimate the coefficients of these linear models; and to recognize or classify image motions from the estimated coefficients. We consider two broad classes of motions: i) generic “motion features” such as motion discontinuities and moving bars; and ii) non-rigid, object-specific, motions such as the motion of human mouths. For motion features we construct a basis of steerable flow fields that approximate the motion features. For object-specific motions we construct basis flow fields from example motions using principal component analysis. In both cases, the model coefficients can be estimated directly from spatiotemporal image derivatives with a robust, multi-resolution scheme. Finally, we show how these model coefficients can be use to detect and recognize specific motions such as occlusion boundaries and facial expressions.

pdf [BibTex]

Fleet, D. J., Black, M. J., Yacoob, Y., Jepson, A. D. Design and use of linear models for image motion analysis Int. J. of Computer Vision, 36(3):171-193, 2000 (article)

pdf [BibTex]

Thumb xl bildschirmfoto 2012 12 06 um 09.48.16
Robustly estimating changes in image appearance

Black, M. J., Fleet, D. J., Yacoob, Y.

Computer Vision and Image Understanding, 78(1):8-31, 2000 (article)

Abstract
We propose a generalized model of image “appearance change” in which brightness variation over time is represented as a probabilistic mixture of different causes. We define four generative models of appearance change due to (1) object or camera motion; (2) illumination phenomena; (3) specular reflections; and (4) “iconic changes” which are specific to the objects being viewed. These iconic changes include complex occlusion events and changes in the material properties of the objects. We develop a robust statistical framework for recovering these appearance changes in image sequences. This approach generalizes previous work on optical flow to provide a richer description of image events and more reliable estimates of image motion in the presence of shadows and specular reflections.

pdf pdf from publisher DOI [BibTex]

Black, M. J., Fleet, D. J., Yacoob, Y. Robustly estimating changes in image appearance Computer Vision and Image Understanding, 78(1):8-31, 2000 (article)

pdf pdf from publisher DOI [BibTex]

1999

Thumb xl bildschirmfoto 2012 12 06 um 09.38.15
Parameterized modeling and recognition of activities

Yacoob, Y., Black, M. J.

Computer Vision and Image Understanding, 73(2):232-247, 1999 (article)

Abstract
In this paper we consider a class of human activities—atomic activities—which can be represented as a set of measurements over a finite temporal window (e.g., the motion of human body parts during a walking cycle) and which has a relatively small space of variations in performance. A new approach for modeling and recognition of atomic activities that employs principal component analysis and analytical global transformations is proposed. The modeling of sets of exemplar instances of activities that are similar in duration and involve similar body part motions is achieved by parameterizing their representation using principal component analysis. The recognition of variants of modeled activities is achieved by searching the space of admissible parameterized transformations that these activities can undergo. This formulation iteratively refines the recognition of the class to which the observed activity belongs and the transformation parameters that relate it to the model in its class. We provide several experiments on recognition of articulated and deformable human motions from image motion parameters.

pdf pdf from publisher DOI [BibTex]

1999

Yacoob, Y., Black, M. J. Parameterized modeling and recognition of activities Computer Vision and Image Understanding, 73(2):232-247, 1999 (article)

pdf pdf from publisher DOI [BibTex]

1997

Thumb xl yasersmile
Recognizing facial expressions in image sequences using local parameterized models of image motion

Black, M. J., Yacoob, Y.

Int. Journal of Computer Vision, 25(1):23-48, 1997 (article)

Abstract
This paper explores the use of local parametrized models of image motion for recovering and recognizing the non-rigid and articulated motion of human faces. Parametric flow models (for example affine) are popular for estimating motion in rigid scenes. We observe that within local regions in space and time, such models not only accurately model non-rigid facial motions but also provide a concise description of the motion in terms of a small number of parameters. These parameters are intuitively related to the motion of facial features during facial expressions and we show how expressions such as anger, happiness, surprise, fear, disgust, and sadness can be recognized from the local parametric motions in the presence of significant head motion. The motion tracking and expression recognition approach performed with high accuracy in extensive laboratory experiments involving 40 subjects as well as in television and movie sequences.

pdf pdf from publisher abstract video [BibTex]