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Abstract: Performance capture is a term for a new class of acquisition methods that enable the simultaneous reconstruction of dynamic shape and surface appearance properties of dynamic scenes - in particular scenes involving human actors - from multi-view video. These reconstruction approaches exceed the capabilities of marker-based optical motion capture approaches that are still state-of-the-art in many real production scenarios, such as in movie productions. While marker-based systems can, at best, reconstruct skeletal motion or very sparse descriptions of non-rigid deformation, it is now possible to extract skeletal motion and full dynamic scene geometry, e.g., the shape of a person in general apparel, like a skirt, including wrinkles and folds on the surface. Even though performance capture algorithms deliver much richer data than traditional marker-based motion capture, many limitations remain and research is still at an early stage.
In our lab, we have been investigating model-based dynamic scene reconstruction methods that employ a template mesh to track a person in a scene. Recently, we tried to answer the question how model-based approaches can be extended to overcome some of the many limitations that still exist. For instance, we developed a new approach that enables us to capture skeletal motion and deforming surface geometry of several closely interacting people, which has been a challenge so far. We also developed a new approach to reconstruct fully-animatable models of humans from video. Since we detect cloth in the captured performances, and estimate paramaters of a physics-based cloth simulation model, we can change skeletal motion during postprocessing and obtain new animations of the entire character with plausible deformations. I will also briefly touch on some ideas we had about how to use performance capture in other application domains, such as model-based video editing and the synthesis of video-based characters, i.e., videos of a humans performing previously unseen user-designed motions.
A lot of his research is about problems that lie on the boundary
between the fields of Computer Vision and Computer Graphics, such as
dynamic 3D scene reconstruction and marker-less motion capture,
computer animation, appearance and reflectance modeling, machine
learning for graphics and vision, new sensors for 3D acquisition,
advanced video processing, as well as image- and physically-based
rendering. For his work, he received several awards including the Otto
Hahn Medal of the Max-Planck Society in 2007, and the EUROGRAPHICS
Young Researcher Award in 2009.
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Title: | Invited Talk: Model-based Performance Capture from Video |
Date: | 23 May 2011 |
Location: | MI 03.13.010 |
Abstract: | Christian Theobalt is the head of the research group 'Graphics; Vision and Video' at the Max-Planck-Institut Informatik and a Professor of Computer Science at Saarland University, Saarbruecken, Germany. Abstract: Performance capture is a term for a new class of acquisition methods that enable the simultaneous reconstruction of dynamic shape and surface appearance properties of dynamic scenes - in particular scenes involving human actors - from multi-view video. These reconstruction approaches exceed the capabilities of marker-based optical motion capture approaches that are still state-of-the-art in many real production scenarios, such as in movie productions... |
Imageurl: | ![]() |
Type: | News |
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