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Hands in Action


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Dimitris Tzionas (Project leader)
Postdoctoral Researcher
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Jürgen Gall
Alumni
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Javier Romero
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Luca Ballan
ETH Zurich
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Aparna Taneja
ETH Zurich
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Luc Van Gool
ETH Zurich
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Marc Pollefeys
ETH Zurich
8 results

2017

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Capturing Hand-Object Interaction and Reconstruction of Manipulated Objects

Tzionas, D.

University of Bonn, 2017 (phdthesis)

Abstract
Hand motion capture with an RGB-D sensor gained recently a lot of research attention, however, even most recent approaches focus on the case of a single isolated hand. We focus instead on hands that interact with other hands or with a rigid or articulated object. Our framework successfully captures motion in such scenarios by combining a generative model with discriminatively trained salient points, collision detection and physics simulation to achieve a low tracking error with physically plausible poses. All components are unified in a single objective function that can be optimized with standard optimization techniques. We initially assume a-priori knowledge of the object's shape and skeleton. In case of unknown object shape there are existing 3d reconstruction methods that capitalize on distinctive geometric or texture features. These methods though fail for textureless and highly symmetric objects like household articles, mechanical parts or toys. We show that extracting 3d hand motion for in-hand scanning effectively facilitates the reconstruction of such objects and we fuse the rich additional information of hands into a 3d reconstruction pipeline. Finally, although shape reconstruction is enough for rigid objects, there is a lack of tools that build rigged models of articulated objects that deform realistically using RGB-D data. We propose a method that creates a fully rigged model consisting of a watertight mesh, embedded skeleton and skinning weights by employing a combination of deformable mesh tracking, motion segmentation based on spectral clustering and skeletonization based on mean curvature flow.

Thesis link (url) Project Page [BibTex]

2016

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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), 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.

Website pdf DOI Project Page [BibTex]

2016

Tzionas, D., Ballan, L., Srikantha, A., Aponte, P., Pollefeys, M., Gall, J. Capturing Hands in Action using Discriminative Salient Points and Physics Simulation International Journal of Computer Vision (IJCV), 2016 (article)

Website pdf DOI Project Page [BibTex]

2015

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3D Object Reconstruction from Hand-Object Interactions

Tzionas, D., Gall, J.

In International Conference on Computer Vision (ICCV), December 2015 (inproceedings)

Abstract
Recent advances have enabled 3d object reconstruction approaches using a single off-the-shelf RGB-D camera. Although these approaches are successful for a wide range of object classes, they rely on stable and distinctive geometric or texture features. Many objects like mechanical parts, toys, household or decorative articles, however, are textureless and characterized by minimalistic shapes that are simple and symmetric. Existing in-hand scanning systems and 3d reconstruction techniques fail for such symmetric objects in the absence of highly distinctive features. In this work, we show that extracting 3d hand motion for in-hand scanning effectively facilitates the reconstruction of even featureless and highly symmetric objects and we present an approach that fuses the rich additional information of hands into a 3d reconstruction pipeline, significantly contributing to the state-of-the-art of in-hand scanning.

pdf Project's Website Video Spotlight Extended Abstract YouTube DOI Project Page [BibTex]

2014

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Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points

Tzionas, D., Srikantha, A., Aponte, P., Gall, J.

In German Conference on Pattern Recognition (GCPR), pages: 1-13, Lecture Notes in Computer Science, Springer, September 2014 (inproceedings)

Abstract
Hand motion capture has been an active research topic in recent years, following the success of full-body pose tracking. Despite similarities, hand tracking proves to be more challenging, characterized by a higher dimensionality, severe occlusions and self-similarity between fingers. For this reason, most approaches rely on strong assumptions, like hands in isolation or expensive multi-camera systems, that limit the practical use. In this work, we propose a framework for hand tracking that can capture the motion of two interacting hands using only a single, inexpensive RGB-D camera. Our approach combines a generative model with collision detection and discriminatively learned salient points. We quantitatively evaluate our approach on 14 new sequences with challenging interactions.

pdf Supplementary pdf Supplementary Material Project Page DOI Project Page [BibTex]

2014

Tzionas, D., Srikantha, A., Aponte, P., Gall, J. Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points In German Conference on Pattern Recognition (GCPR), pages: 1-13, Lecture Notes in Computer Science, Springer, September 2014 (inproceedings)

pdf Supplementary pdf Supplementary Material Project Page DOI Project Page [BibTex]

2013

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A Comparison of Directional Distances for Hand Pose Estimation

Tzionas, D., Gall, J.

In German Conference on Pattern Recognition (GCPR), 8142, pages: 131-141, Lecture Notes in Computer Science, (Editors: Weickert, Joachim and Hein, Matthias and Schiele, Bernt), Springer, 2013 (inproceedings)

Abstract
Benchmarking methods for 3d hand tracking is still an open problem due to the difficulty of acquiring ground truth data. We introduce a new dataset and benchmarking protocol that is insensitive to the accumulative error of other protocols. To this end, we create testing frame pairs of increasing difficulty and measure the pose estimation error separately for each of them. This approach gives new insights and allows to accurately study the performance of each feature or method without employing a full tracking pipeline. Following this protocol, we evaluate various directional distances in the context of silhouette-based 3d hand tracking, expressed as special cases of a generalized Chamfer distance form. An appropriate parameter setup is proposed for each of them, and a comparative study reveals the best performing method in this context.

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

2013

Tzionas, D., Gall, J. A Comparison of Directional Distances for Hand Pose Estimation In German Conference on Pattern Recognition (GCPR), 8142, pages: 131-141, Lecture Notes in Computer Science, (Editors: Weickert, Joachim and Hein, Matthias and Schiele, Bernt), Springer, 2013 (inproceedings)

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

2012

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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]

2012

Ballan, L., Taneja, A., Gall, J., van Gool, L., Pollefeys, M. Motion Capture of Hands in Action using Discriminative Salient Points In European Conference on Computer Vision (ECCV), 7577, pages: 640-653, LNCS, Springer, 2012 (inproceedings)

data video pdf supplementary Project Page [BibTex]

2010

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Spatio-Temporal Modeling of Grasping Actions

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

In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pages: 2103-2108, 2010 (inproceedings)

Pdf Project Page [BibTex]

2010

Romero, J., Feix, T., Kjellström, H., Kragic, D. Spatio-Temporal Modeling of Grasping Actions In IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS, pages: 2103-2108, 2010 (inproceedings)

Pdf Project Page [BibTex]

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Hands in action: real-time 3D reconstruction of hands in interaction with objects

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

In IEEE International Conference on Robotics and Automation (ICRA), pages: 458-463, 2010 (inproceedings)

Pdf Project Page [BibTex]

Romero, J., Kjellström, H., Kragic, D. Hands in action: real-time 3D reconstruction of hands in interaction with objects In IEEE International Conference on Robotics and Automation (ICRA), pages: 458-463, 2010 (inproceedings)

Pdf Project Page [BibTex]

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