C
Christian Theobalt
Researcher at Max Planck Society
Publications - 508
Citations - 34680
Christian Theobalt is an academic researcher from Max Planck Society. The author has contributed to research in topics: Motion capture & Computer science. The author has an hindex of 89, co-authored 450 publications receiving 25487 citations. Previous affiliations of Christian Theobalt include Stanford University & Facebook.
Papers
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Journal ArticleDOI
PhysCap: physically plausible monocular 3D motion capture in real time
TL;DR: In this article, a CNN infers 2D and 3D joint positions, and subsequently, an inverse kinematics step finds space-time coherent joint angles and global 3D pose.
Book ChapterDOI
Full body performance capture under uncontrolled and varying illumination: a shading-based approach
TL;DR: A marker-less method for full body human performance capture by analyzing shading information from a sequence of multi-view images, which are recorded under uncontrolled and changing lighting conditions, and is applicable in cases where background segmentation cannot be performed or a set of training poses is unavailable.
Proceedings ArticleDOI
Combining 2D feature tracking and volume reconstruction for online video-based human motion capture
TL;DR: A system to capture human motion at interactive frame rates without the use of markers or scene-intruding devices is described and a multilayer hierarchical kinematic skeleton is fitted to each frame in a two-stage process.
Journal ArticleDOI
Capturing Relightable Human Performances under General Uncontrolled Illumination
Guannan Li,Chenglei Wu,Carsten Stoll,Yebin Liu,Kiran Varanasi,Qionghai Dai,Christian Theobalt +6 more
TL;DR: The method enables plausible reconstruction of relightable dynamic scene models without a complex controlled lighting apparatus, and opens up a path towards relightingable performance capture in less constrained environments and using less complex acquisition setups.
Posted Content
Neural Human Video Rendering by Learning Dynamic Textures and Rendering-to-Video Translation.
Lingjie Liu,Weipeng Xu,Marc Habermann,Michael Zollhoefer,Florian Bernard,Hyeongwoo Kim,Wenping Wang,Christian Theobalt +7 more
TL;DR: A novel human video synthesis method that approaches limiting factors by explicitly disentangling the learning of time-coherent fine-scale details from the embedding of the human in 2D screen space and shows significant improvement over the state of the art both qualitatively and quantitatively.