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.
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Proceedings ArticleDOI
Face2Face: Real-Time Face Capture and Reenactment of RGB Videos
TL;DR: A novel approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video) that addresses the under-constrained problem of facial identity recovery from monocular video by non-rigid model-based bundling and re-render the manipulated output video in a photo-realistic fashion.
Journal ArticleDOI
VNect: real-time 3D human pose estimation with a single RGB camera
Dushyant Mehta,Srinath Sridhar,Oleksandr Sotnychenko,Helge Rhodin,Mohammad Shafiei,Hans-Peter Seidel,Weipeng Xu,Dan Casas,Christian Theobalt +8 more
TL;DR: In this paper, a fully-convolutional pose formulation was proposed to regress 2D and 3D joint positions jointly in real-time and does not require tightly cropped input frames.
Journal ArticleDOI
BundleFusion: real-time globally consistent 3D reconstruction using on-the-fly surface re-integration
TL;DR: In this paper, a robust pose estimation strategy is proposed for real-time, high-quality, 3D scanning of large-scale scenes using RGB-D input with an efficient hierarchical approach, which removes heavy reliance on temporal tracking and continually localizes to the globally optimized frames instead.
Proceedings ArticleDOI
Free-viewpoint video of human actors
TL;DR: A system that uses multi-view synchronized video footage of an actor's performance to estimate motion parameters and to interactively re-render the actor's appearance from any viewpoint, yielding a highly naturalistic impression of the actor.
Journal ArticleDOI
VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera
Dushyant Mehta,Srinath Sridhar,Oleksandr Sotnychenko,Helge Rhodin,Mohammad Shafiei,Hans-Peter Seidel,Weipeng Xu,Dan Casas,Christian Theobalt +8 more
TL;DR: This work presents the first real-time method to capture the full global 3D skeletal pose of a human in a stable, temporally consistent manner using a single RGB camera and shows that the approach is more broadly applicable than RGB-D solutions, i.e., it works for outdoor scenes, community videos, and low quality commodity RGB cameras.