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
Estimating Egocentric 3D Human Pose in the Wild with External Weak Supervision
TL;DR: A new egocentric pose estimation method which can be trained on the new dataset with weak external supervision and outperforms the state-of-the-art methods both quantitatively and qualitatively.
Proceedings ArticleDOI
Device effect on panoramic video+context tasks
TL;DR: It is discovered that, in the authors' complex reasoning task, HMDs are as effective as desktop displays even if participants felt less capable, but tablets were less effective thandesktop displays even though participants felt just as capable.
Proceedings ArticleDOI
MoCapDeform: Monocular 3D Human Motion Capture in Deformable Scenes
TL;DR: This paper proposes MoCapDeform, a new framework for monocular 3D human motion capture that is the first to explicitly model non-rigid deformations of a 3D scene for improved3D human pose estimation and deformable environment reconstruction.
Journal ArticleDOI
Performance Capture of High-Speed Motion Using Staggered Multi-View Recording
TL;DR: This work presents a markerless performance capture system that can acquire the motion and the texture of human actors performing fast movements using only commodity hardware and introduces a model‐based deblurring algorithm which is able to handle disocclusion, self‐occlusion and complex object motions.
Proceedings ArticleDOI
Joint motion and reflectance capture for relightable 3D video
Christian Theobalt,Naveed Ahmed,Edilson de Aguiar,Gernot Ziegler,Hendrik P. A. Lensch,Marcus Magnor,Hans-Peter Seidel +6 more
TL;DR: A video-based modeling approach that captures human motion as well as reflectance characteristics from a handful of synchronized video recordings is developed and is able to recover spatially varying reflectance properties of clothes by exploiting the time-varying orientation of each surface point with respect to camera and light direction.