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2019


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

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

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

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

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paper pdf DOI [BibTex]

2019


paper pdf DOI [BibTex]


Active Perception based Formation Control for Multiple Aerial Vehicles
Active Perception based Formation Control for Multiple Aerial Vehicles

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

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

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

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pdf DOI Project Page [BibTex]

pdf DOI Project Page [BibTex]


3D Morphable Face Models - Past, Present and Future
3D Morphable Face Models - Past, Present and Future

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

arxiv preprint arXiv:1909.01815, September 2019 (article)

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

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paper project page [BibTex]

paper project page [BibTex]


Implementation of a 6-{DOF} Parallel Continuum Manipulator for Delivering Fingertip Tactile Cues
Implementation of a 6-DOF Parallel Continuum Manipulator for Delivering Fingertip Tactile Cues

Young, E. M., Kuchenbecker, K. J.

IEEE Transactions on Haptics, 12(3):295-306, June 2019 (article)

Abstract
Existing fingertip haptic devices can deliver different subsets of tactile cues in a compact package, but we have not yet seen a wearable six-degree-of-freedom (6-DOF) display. This paper presents the Fuppeteer (short for Fingertip Puppeteer), a device that is capable of controlling the position and orientation of a flat platform, such that any combination of normal and shear force can be delivered at any location on any human fingertip. We build on our previous work of designing a parallel continuum manipulator for fingertip haptics by presenting a motorized version in which six flexible Nitinol wires are actuated via independent roller mechanisms and proportional-derivative controllers. We evaluate the settling time and end-effector vibrations observed during system responses to step inputs. After creating a six-dimensional lookup table and adjusting simulated inputs using measured Jacobians, we show that the device can make contact with all parts of the fingertip with a mean error of 1.42 mm. Finally, we present results from a human-subject study. A total of 24 users discerned 9 evenly distributed contact locations with an average accuracy of 80.5%. Translational and rotational shear cues were identified reasonably well near the center of the fingertip and more poorly around the edges.

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DOI [BibTex]


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

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

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

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

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pdf Journal DOI [BibTex]

pdf Journal DOI [BibTex]


 Perceptual Effects of Inconsistency in Human Animations
Perceptual Effects of Inconsistency in Human Animations

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

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

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

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publisher pdf DOI [BibTex]

publisher pdf DOI [BibTex]


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How Does It Feel to Clap Hands with a Robot?

Fitter, N. T., Kuchenbecker, K. J.

International Journal of Social Robotics, 2019 (article) Accepted

Abstract
Future robots may need lighthearted physical interaction capabilities to connect with people in meaningful ways. To begin exploring how users perceive playful human–robot hand-to-hand interaction, we conducted a study with 20 participants. Each user played simple hand-clapping games with the Rethink Robotics Baxter Research Robot during a 1-h-long session involving 24 randomly ordered conditions that varied in facial reactivity, physical reactivity, arm stiffness, and clapping tempo. Survey data and experiment recordings demonstrate that this interaction is viable: all users successfully completed the experiment and mentioned enjoying at least one game without prompting. Hand-clapping tempo was highly salient to users, and human-like robot errors were more widely accepted than mechanical errors. Furthermore, perceptions of Baxter varied in the following statistically significant ways: facial reactivity increased the robot’s perceived pleasantness and energeticness; physical reactivity decreased pleasantness, energeticness, and dominance; higher arm stiffness increased safety and decreased dominance; and faster tempo increased energeticness and increased dominance. These findings can motivate and guide roboticists who want to design social–physical human–robot interactions.

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[BibTex]

[BibTex]


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

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

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

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

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

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


Probabilistic Linear Solvers: A Unifying View
Probabilistic Linear Solvers: A Unifying View

Bartels, S., Cockayne, J., Ipsen, I. C. F., Hennig, P.

Statistics and Computing, 2019 (article) Accepted

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link (url) [BibTex]

link (url) [BibTex]

2017


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Evaluation of High-Fidelity Simulation as a Training Tool in Transoral Robotic Surgery

Bur, A. M., Gomez, E. D., Newman, J. G., Weinstein, G. S., Bert W. O’Malley, J., Rassekh, C. H., Kuchenbecker, K. J.

Laryngoscope, 127(12):2790-2795, December 2017 (article)

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DOI [BibTex]

2017


DOI [BibTex]


Probabilistic Line Searches for Stochastic Optimization
Probabilistic Line Searches for Stochastic Optimization

Mahsereci, M., Hennig, P.

Journal of Machine Learning Research, 18(119):1-59, November 2017 (article)

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Learning a model of facial shape and expression from {4D} scans
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).

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

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


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

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publisher DOI Project Page [BibTex]


Embodied Hands: Modeling and Capturing Hands and Bodies Together
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.

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

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


An Online Scalable Approach to Unified Multirobot Cooperative Localization and Object Tracking
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.

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Published Version link (url) DOI [BibTex]

Published Version link (url) DOI [BibTex]


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Using Contact Forces and Robot Arm Accelerations to Automatically Rate Surgeon Skill at Peg Transfer

Brown, J. D., O’Brien, C. E., Leung, S. C., Dumon, K. R., Lee, D. I., Kuchenbecker, K. J.

IEEE Transactions on Biomedical Engineering, 64(9):2263-2275, September 2017 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Ungrounded Haptic Augmented Reality System for Displaying Texture and Friction

Culbertson, H., Kuchenbecker, K. J.

IEEE/ASME Transactions on Mechatronics, 22(4):1839-1849, August 2017 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Perception of Force and Stiffness in the Presence of Low-Frequency Haptic Noise

Gurari, N., Okamura, A. M., Kuchenbecker, K. J.

PLoS ONE, 12(6):e0178605, June 2017 (article)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Evaluation of a Vibrotactile Simulator for Dental Caries Detection

Kuchenbecker, K. J., Parajon, R., Maggio, M. P.

Simulation in Healthcare, 12(3):148-156, June 2017 (article)

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DOI [BibTex]

DOI [BibTex]


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Importance of Matching Physical Friction, Hardness, and Texture in Creating Realistic Haptic Virtual Surfaces

Culbertson, H., Kuchenbecker, K. J.

IEEE Transactions on Haptics, 10(1):63-74, January 2017 (article)

hi

[BibTex]


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Effects of Grip-Force, Contact, and Acceleration Feedback on a Teleoperated Pick-and-Place Task

Khurshid, R. P., Fitter, N. T., Fedalei, E. A., Kuchenbecker, K. J.

IEEE Transactions on Haptics, 10(1):40-53, January 2017 (article)

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[BibTex]

[BibTex]


Early Stopping Without a Validation Set
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.

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link (url) Project Page Project Page [BibTex]


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Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning

Roos, F. D., Hennig, P.

arXiv preprint arXiv:1706.00241, 2017 (article)

Abstract
Solving symmetric positive definite linear problems is a fundamental computational task in machine learning. The exact solution, famously, is cubicly expensive in the size of the matrix. To alleviate this problem, several linear-time approximations, such as spectral and inducing-point methods, have been suggested and are now in wide use. These are low-rank approximations that choose the low-rank space a priori and do not refine it over time. While this allows linear cost in the data-set size, it also causes a finite, uncorrected approximation error. Authors from numerical linear algebra have explored ways to iteratively refine such low-rank approximations, at a cost of a small number of matrix-vector multiplications. This idea is particularly interesting in the many situations in machine learning where one has to solve a sequence of related symmetric positive definite linear problems. From the machine learning perspective, such deflation methods can be interpreted as transfer learning of a low-rank approximation across a time-series of numerical tasks. We study the use of such methods for our field. Our empirical results show that, on regression and classification problems of intermediate size, this approach can interpolate between low computational cost and numerical precision.

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link (url) Project Page [BibTex]


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Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings

Kanagawa, M., Sriperumbudur, B. K., Fukumizu, K.

Arxiv e-prints, arXiv:1709.00147v1 [math.NA], 2017 (article)

Abstract
This paper presents convergence analysis of kernel-based quadrature rules in misspecified settings, focusing on deterministic quadrature in Sobolev spaces. In particular, we deal with misspecified settings where a test integrand is less smooth than a Sobolev RKHS based on which a quadrature rule is constructed. We provide convergence guarantees based on two different assumptions on a quadrature rule: one on quadrature weights, and the other on design points. More precisely, we show that convergence rates can be derived (i) if the sum of absolute weights remains constant (or does not increase quickly), or (ii) if the minimum distance between distance design points does not decrease very quickly. As a consequence of the latter result, we derive a rate of convergence for Bayesian quadrature in misspecified settings. We reveal a condition on design points to make Bayesian quadrature robust to misspecification, and show that, under this condition, it may adaptively achieve the optimal rate of convergence in the Sobolev space of a lesser order (i.e., of the unknown smoothness of a test integrand), under a slightly stronger regularity condition on the integrand.

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arXiv [BibTex]

arXiv [BibTex]


Data-Driven Physics for Human Soft Tissue Animation
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.

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video paper link (url) Project Page [BibTex]

video paper link (url) Project Page [BibTex]


Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs
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

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video pdf Project Page [BibTex]

video pdf Project Page [BibTex]


Efficient 2D and 3D Facade Segmentation using Auto-Context
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.

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arXiv Project Page [BibTex]

arXiv Project Page [BibTex]


{ClothCap}: Seamless {4D} Clothing Capture and Retargeting
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.

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

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


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Efficiency of analytical and sampling-based uncertainty propagation in intensity-modulated proton therapy

Wahl, N., Hennig, P., Wieser, H. P., Bangert, M.

Physics in Medicine & Biology, 62(14):5790-5807, 2017 (article)

Abstract
The sensitivity of intensity-modulated proton therapy (IMPT) treatment plans to uncertainties can be quantified and mitigated with robust/min-max and stochastic/probabilistic treatment analysis and optimization techniques. Those methods usually rely on sparse random, importance, or worst-case sampling. Inevitably, this imposes a trade-off between computational speed and accuracy of the uncertainty propagation. Here, we investigate analytical probabilistic modeling (APM) as an alternative for uncertainty propagation and minimization in IMPT that does not rely on scenario sampling. APM propagates probability distributions over range and setup uncertainties via a Gaussian pencil-beam approximation into moments of the probability distributions over the resulting dose in closed form. It supports arbitrary correlation models and allows for efficient incorporation of fractionation effects regarding random and systematic errors. We evaluate the trade-off between run-time and accuracy of APM uncertainty computations on three patient datasets. Results are compared against reference computations facilitating importance and random sampling. Two approximation techniques to accelerate uncertainty propagation and minimization based on probabilistic treatment plan optimization are presented. Runtimes are measured on CPU and GPU platforms, dosimetric accuracy is quantified in comparison to a sampling-based benchmark (5000 random samples). APM accurately propagates range and setup uncertainties into dose uncertainties at competitive run-times (GPU ##IMG## [http://ej.iop.org/images/0031-9155/62/14/5790/pmbaa6ec5ieqn001.gif] {$\leqslant {5}$} min). The resulting standard deviation (expectation value) of dose show average global ##IMG## [http://ej.iop.org/images/0031-9155/62/14/5790/pmbaa6ec5ieqn002.gif] {$\gamma_{{3}\% / {3}~{\rm mm}}$} pass rates between 94.2% and 99.9% (98.4% and 100.0%). All investigated importance sampling strategies provided less accuracy at higher run-times considering only a single fraction. Considering fractionation, APM uncertainty propagation and treatment plan optimization was proven to be possible at constant time complexity, while run-times of sampling-based computations are linear in the number of fractions. Using sum sampling within APM, uncertainty propagation can only be accelerated at the cost of reduced accuracy in variance calculations. For probabilistic plan optimization, we were able to approximate the necessary pre-computations within seconds, yielding treatment plans of similar quality as gained from exact uncertainty propagation. APM is suited to enhance the trade-off between speed and accuracy in uncertainty propagation and probabilistic treatment plan optimization, especially in the context of fractionation. This brings fully-fledged APM computations within reach of clinical application.

pn

link (url) [BibTex]

link (url) [BibTex]


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Analytical probabilistic modeling of RBE-weighted dose for ion therapy

Wieser, H., Hennig, P., Wahl, N., Bangert, M.

Physics in Medicine and Biology (PMB), 62(23):8959-8982, 2017 (article)

pn

link (url) [BibTex]

link (url) [BibTex]

2016


Creating body shapes from verbal descriptions by linking similarity spaces
Creating body shapes from verbal descriptions by linking similarity spaces

Hill, M. Q., Streuber, S., Hahn, C. A., Black, M. J., O’Toole, A. J.

Psychological Science, 27(11):1486-1497, November 2016, (article)

Abstract
Brief verbal descriptions of bodies (e.g. curvy, long-legged) can elicit vivid mental images. The ease with which we create these mental images belies the complexity of three-dimensional body shapes. We explored the relationship between body shapes and body descriptions and show that a small number of words can be used to generate categorically accurate representations of three-dimensional bodies. The dimensions of body shape variation that emerged in a language-based similarity space were related to major dimensions of variation computed directly from three-dimensional laser scans of 2094 bodies. This allowed us to generate three-dimensional models of people in the shape space using only their coordinates on analogous dimensions in the language-based description space. Human descriptions of photographed bodies and their corresponding models matched closely. The natural mapping between the spaces illustrates the role of language as a concise code for body shape, capturing perceptually salient global and local body features.

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pdf [BibTex]

2016


pdf [BibTex]


{Body Talk}: Crowdshaping Realistic {3D} Avatars with Words
Body Talk: Crowdshaping Realistic 3D Avatars with Words

Streuber, S., Quiros-Ramirez, M. A., Hill, M. Q., Hahn, C. A., Zuffi, S., O’Toole, A., Black, M. J.

ACM Trans. Graph. (Proc. SIGGRAPH), 35(4):54:1-54:14, July 2016 (article)

Abstract
Realistic, metrically accurate, 3D human avatars are useful for games, shopping, virtual reality, and health applications. Such avatars are not in wide use because solutions for creating them from high-end scanners, low-cost range cameras, and tailoring measurements all have limitations. Here we propose a simple solution and show that it is surprisingly accurate. We use crowdsourcing to generate attribute ratings of 3D body shapes corresponding to standard linguistic descriptions of 3D shape. We then learn a linear function relating these ratings to 3D human shape parameters. Given an image of a new body, we again turn to the crowd for ratings of the body shape. The collection of linguistic ratings of a photograph provides remarkably strong constraints on the metric 3D shape. We call the process crowdshaping and show that our Body Talk system produces shapes that are perceptually indistinguishable from bodies created from high-resolution scans and that the metric accuracy is sufficient for many tasks. This makes body “scanning” practical without a scanner, opening up new applications including database search, visualization, and extracting avatars from books.

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pdf web tool video talk (ppt) [BibTex]

pdf web tool video talk (ppt) [BibTex]


Capturing Hands in Action using Discriminative Salient Points and Physics Simulation
Capturing Hands in Action using Discriminative Salient Points and Physics Simulation

Tzionas, D., Ballan, L., Srikantha, A., Aponte, P., Pollefeys, M., Gall, J.

International Journal of Computer Vision (IJCV), 118(2):172-193, June 2016 (article)

Abstract
Hand motion capture is a popular research field, recently gaining more attention due to the ubiquity of RGB-D sensors. However, even most recent approaches focus on the case of a single isolated hand. In this work, we focus on hands that interact with other hands or objects and present a framework that successfully captures motion in such interaction scenarios for both rigid and articulated objects. Our framework combines a generative model with discriminatively trained salient points to achieve a low tracking error and with collision detection and physics simulation to achieve physically plausible estimates even in case of occlusions and missing visual data. Since all components are unified in a single objective function which is almost everywhere differentiable, it can be optimized with standard optimization techniques. Our approach works for monocular RGB-D sequences as well as setups with multiple synchronized RGB cameras. For a qualitative and quantitative evaluation, we captured 29 sequences with a large variety of interactions and up to 150 degrees of freedom.

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Website pdf link (url) DOI Project Page [BibTex]

Website pdf link (url) DOI Project Page [BibTex]


Human Pose Estimation from Video and IMUs
Human Pose Estimation from Video and IMUs

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

Transactions on Pattern Analysis and Machine Intelligence PAMI, 38(8):1533-1547, January 2016 (article)

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data pdf dataset_documentation [BibTex]

data pdf dataset_documentation [BibTex]


Gaussian Process-Based Predictive Control for Periodic Error Correction
Gaussian Process-Based Predictive Control for Periodic Error Correction

Klenske, E. D., Zeilinger, M., Schölkopf, B., Hennig, P.

IEEE Transactions on Control Systems Technology , 24(1):110-121, 2016 (article)

ei pn

PDF DOI [BibTex]

PDF DOI [BibTex]


Dual Control for Approximate Bayesian Reinforcement Learning
Dual Control for Approximate Bayesian Reinforcement Learning

Klenske, E. D., Hennig, P.

Journal of Machine Learning Research, 17(127):1-30, 2016 (article)

ei pn

PDF link (url) [BibTex]

PDF link (url) [BibTex]


Shape estimation of subcutaneous adipose tissue using an articulated statistical shape model
Shape estimation of subcutaneous adipose tissue using an articulated statistical shape model

Yeo, S. Y., Romero, J., Loper, M., Machann, J., Black, M.

Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 0(0):1-8, 2016 (article)

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publisher website preprint pdf link (url) DOI Project Page [BibTex]

publisher website preprint pdf link (url) DOI Project Page [BibTex]


The GRASP Taxonomy of Human Grasp Types
The GRASP Taxonomy of Human Grasp Types

Feix, T., Romero, J., Schmiedmayer, H., Dollar, A., Kragic, D.

Human-Machine Systems, IEEE Transactions on, 46(1):66-77, 2016 (article)

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publisher website pdf DOI Project Page [BibTex]

publisher website pdf DOI Project Page [BibTex]


Map-Based Probabilistic Visual Self-Localization
Map-Based Probabilistic Visual Self-Localization

Brubaker, M. A., Geiger, A., Urtasun, R.

IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 2016 (article)

Abstract
Accurate and efficient self-localization is a critical problem for autonomous systems. This paper describes an affordable solution to vehicle self-localization which uses odometry computed from two video cameras and road maps as the sole inputs. The core of the method is a probabilistic model for which an efficient approximate inference algorithm is derived. The inference algorithm is able to utilize distributed computation in order to meet the real-time requirements of autonomous systems in some instances. Because of the probabilistic nature of the model the method is capable of coping with various sources of uncertainty including noise in the visual odometry and inherent ambiguities in the map (e.g., in a Manhattan world). By exploiting freely available, community developed maps and visual odometry measurements, the proposed method is able to localize a vehicle to 4m on average after 52 seconds of driving on maps which contain more than 2,150km of drivable roads.

avg ps

pdf Project Page [BibTex]

pdf Project Page [BibTex]

2015


Scalable Robust Principal Component Analysis using {Grassmann} Averages
Scalable Robust Principal Component Analysis using Grassmann Averages

Hauberg, S., Feragen, A., Enficiaud, R., Black, M.

IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), December 2015 (article)

Abstract
In large datasets, manual data verification is impossible, and we must expect the number of outliers to increase with data size. While principal component analysis (PCA) can reduce data size, and scalable solutions exist, it is well-known that outliers can arbitrarily corrupt the results. Unfortunately, state-of-the-art approaches for robust PCA are not scalable. We note that in a zero-mean dataset, each observation spans a one-dimensional subspace, giving a point on the Grassmann manifold. We show that the average subspace corresponds to the leading principal component for Gaussian data. We provide a simple algorithm for computing this Grassmann Average (GA), and show that the subspace estimate is less sensitive to outliers than PCA for general distributions. Because averages can be efficiently computed, we immediately gain scalability. We exploit robust averaging to formulate the Robust Grassmann Average (RGA) as a form of robust PCA. The resulting Trimmed Grassmann Average (TGA) is appropriate for computer vision because it is robust to pixel outliers. The algorithm has linear computational complexity and minimal memory requirements. We demonstrate TGA for background modeling, video restoration, and shadow removal. We show scalability by performing robust PCA on the entire Star Wars IV movie; a task beyond any current method. Source code is available online.

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preprint pdf from publisher supplemental Project Page [BibTex]

2015


preprint pdf from publisher supplemental Project Page [BibTex]


{SMPL}: A Skinned Multi-Person Linear Model
SMPL: A Skinned Multi-Person Linear Model

Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., Black, M. J.

ACM Trans. Graphics (Proc. SIGGRAPH Asia), 34(6):248:1-248:16, ACM, New York, NY, October 2015 (article)

Abstract
We present a learned model of human body shape and pose-dependent shape variation that is more accurate than previous models and is compatible with existing graphics pipelines. Our Skinned Multi-Person Linear model (SMPL) is a skinned vertex-based model that accurately represents a wide variety of body shapes in natural human poses. The parameters of the model are learned from data including the rest pose template, blend weights, pose-dependent blend shapes, identity-dependent blend shapes, and a regressor from vertices to joint locations. Unlike previous models, the pose-dependent blend shapes are a linear function of the elements of the pose rotation matrices. This simple formulation enables training the entire model from a relatively large number of aligned 3D meshes of different people in different poses. We quantitatively evaluate variants of SMPL using linear or dual-quaternion blend skinning and show that both are more accurate than a Blend-SCAPE model trained on the same data. We also extend SMPL to realistically model dynamic soft-tissue deformations. Because it is based on blend skinning, SMPL is compatible with existing rendering engines and we make it available for research purposes.

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pdf video code/model errata DOI Project Page Project Page [BibTex]

pdf video code/model errata DOI Project Page Project Page [BibTex]


Dyna: A Model of Dynamic Human Shape in Motion
Dyna: A Model of Dynamic Human Shape in Motion

Pons-Moll, G., Romero, J., Mahmood, N., Black, M. J.

ACM Transactions on Graphics, (Proc. SIGGRAPH), 34(4):120:1-120:14, ACM, August 2015 (article)

Abstract
To look human, digital full-body avatars need to have soft tissue deformations like those of real people. We learn a model of soft-tissue deformations from examples using a high-resolution 4D capture system and a method that accurately registers a template mesh to sequences of 3D scans. Using over 40,000 scans of ten subjects, we learn how soft tissue motion causes mesh triangles to deform relative to a base 3D body model. Our Dyna model uses a low-dimensional linear subspace to approximate soft-tissue deformation and relates the subspace coefficients to the changing pose of the body. Dyna uses a second-order auto-regressive model that predicts soft-tissue deformations based on previous deformations, the velocity and acceleration of the body, and the angular velocities and accelerations of the limbs. Dyna also models how deformations vary with a person’s body mass index (BMI), producing different deformations for people with different shapes. Dyna realistically represents the dynamics of soft tissue for previously unseen subjects and motions. We provide tools for animators to modify the deformations and apply them to new stylized characters.

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pdf preprint video data DOI Project Page Project Page [BibTex]

pdf preprint video data DOI Project Page Project Page [BibTex]


Linking Objects to Actions: Encoding of Target Object and Grasping Strategy in Primate Ventral Premotor Cortex
Linking Objects to Actions: Encoding of Target Object and Grasping Strategy in Primate Ventral Premotor Cortex

Vargas-Irwin, C. E., Franquemont, L., Black, M. J., Donoghue, J. P.

Journal of Neuroscience, 35(30):10888-10897, July 2015 (article)

Abstract
Neural activity in ventral premotor cortex (PMv) has been associated with the process of matching perceived objects with the motor commands needed to grasp them. It remains unclear how PMv networks can flexibly link percepts of objects affording multiple grasp options into a final desired hand action. Here, we use a relational encoding approach to track the functional state of PMv neuronal ensembles in macaque monkeys through the process of passive viewing, grip planning, and grasping movement execution. We used objects affording multiple possible grip strategies. The task included separate instructed delay periods for object presentation and grip instruction. This approach allowed us to distinguish responses elicited by the visual presentation of the objects from those associated with selecting a given motor plan for grasping. We show that PMv continuously incorporates information related to object shape and grip strategy as it becomes available, revealing a transition from a set of ensemble states initially most closely related to objects, to a new set of ensemble patterns reflecting unique object-grip combinations. These results suggest that PMv dynamically combines percepts, gradually navigating toward activity patterns associated with specific volitional actions, rather than directly mapping perceptual object properties onto categorical grip representations. Our results support the idea that PMv is part of a network that dynamically computes motor plans from perceptual information. Significance Statement: The present work demonstrates that the activity of groups of neurons in primate ventral premotor cortex reflects information related to visually presented objects, as well as the motor strategy used to grasp them, linking individual objects to multiple possible grips. PMv could provide useful control signals for neuroprosthetic assistive devices designed to interact with objects in a flexible way.

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publisher link DOI Project Page [BibTex]

publisher link DOI Project Page [BibTex]


Multi-view and 3D Deformable Part Models
Multi-view and 3D Deformable Part Models

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

Pattern Analysis and Machine Intelligence, 37(11):14, IEEE, March 2015 (article)

Abstract
As objects are inherently 3-dimensional, they have been modeled in 3D in the early days of computer vision. Due to the ambiguities arising from mapping 2D features to 3D models, 3D object representations have been neglected and 2D feature-based models are the predominant paradigm in object detection nowadays. While such models have achieved outstanding bounding box detection performance, they come with limited expressiveness, as they are clearly limited in their capability of reasoning about 3D shape or viewpoints. In this work, we bring the worlds of 3D and 2D object representations closer, by building an object detector which leverages the expressive power of 3D object representations while at the same time can be robustly matched to image evidence. To that end, we gradually extend the successful deformable part model [1] to include viewpoint information and part-level 3D geometry information, resulting in several different models with different level of expressiveness. We end up with a 3D object model, consisting of multiple object parts represented in 3D and a continuous appearance model. We experimentally verify that our models, while providing richer object hypotheses than the 2D object models, provide consistently better joint object localization and viewpoint estimation than the state-of-the-art multi-view and 3D object detectors on various benchmarks (KITTI [2], 3D object classes [3], Pascal3D+ [4], Pascal VOC 2007 [5], EPFL multi-view cars [6]).

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DOI Project Page [BibTex]

DOI Project Page [BibTex]


{Spike train SIMilarity Space} ({SSIMS}): A framework for single neuron and ensemble data analysis
Spike train SIMilarity Space (SSIMS): A framework for single neuron and ensemble data analysis

Vargas-Irwin, C. E., Brandman, D. M., Zimmermann, J. B., Donoghue, J. P., Black, M. J.

Neural Computation, 27(1):1-31, MIT Press, January 2015 (article)

Abstract
We present a method to evaluate the relative similarity of neural spiking patterns by combining spike train distance metrics with dimensionality reduction. Spike train distance metrics provide an estimate of similarity between activity patterns at multiple temporal resolutions. Vectors of pair-wise distances are used to represent the intrinsic relationships between multiple activity patterns at the level of single units or neuronal ensembles. Dimensionality reduction is then used to project the data into concise representations suitable for clustering analysis as well as exploratory visualization. Algorithm performance and robustness are evaluated using multielectrode ensemble activity data recorded in behaving primates. We demonstrate how Spike train SIMilarity Space (SSIMS) analysis captures the relationship between goal directions for an 8-directional reaching task and successfully segregates grasp types in a 3D grasping task in the absence of kinematic information. The algorithm enables exploration of virtually any type of neural spiking (time series) data, providing similarity-based clustering of neural activity states with minimal assumptions about potential information encoding models.

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pdf: publisher site pdf: author's proof DOI Project Page [BibTex]

pdf: publisher site pdf: author's proof DOI Project Page [BibTex]


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Probabilistic Interpretation of Linear Solvers

Hennig, P.

SIAM Journal on Optimization, 25(1):234-260, 2015 (article)

ei pn

Web PDF link (url) DOI [BibTex]

Web PDF link (url) DOI [BibTex]


Metric Regression Forests for Correspondence Estimation
Metric Regression Forests for Correspondence Estimation

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

International Journal of Computer Vision, pages: 1-13, 2015 (article)

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springer PDF Project Page [BibTex]

springer PDF Project Page [BibTex]


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Probabilistic numerics and uncertainty in computations

Hennig, P., Osborne, M. A., Girolami, M.

Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 471(2179), 2015 (article)

Abstract
We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation with limited time or hardware, are important for much contemporary science and industry. Within applications such as climate science and astrophysics, the need to make decisions on the basis of computations with large and complex data have led to a renewed focus on the management of numerical uncertainty. We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. We then show that the probabilistic view suggests new algorithms that can flexibly be adapted to suit application specifics, while delivering improved empirical performance. We provide concrete illustrations of the benefits of probabilistic numeric algorithms on real scientific problems from astrometry and astronomical imaging, while highlighting open problems with these new algorithms. Finally, we describe how probabilistic numerical methods provide a coherent framework for identifying the uncertainty in calculations performed with a combination of numerical algorithms (e.g. both numerical optimizers and differential equation solvers), potentially allowing the diagnosis (and control) of error sources in computations.

ei pn

PDF DOI [BibTex]

PDF DOI [BibTex]