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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LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration
TL;DR: LoopReg is an end-to-end learning framework to register a corpus of scans to a common 3D human model, and can train LoopRegmainly self-supervised - following a supervised warm-start, the model becomes increasingly more accurate as additional unlabelled raw scans are processed.
Posted Content
Detailed Human Avatars from Monocular Video
TL;DR: In this paper, a parameterized body model is refined and optimized to maximally resemble subjects from a video showing them from all sides, resulting in the most sophisticated-looking human avatars obtained from a single video.
Journal ArticleDOI
HeadOn: Real-time Reenactment of Human Portrait Videos
TL;DR: It is proposed that HeadOn, the first real-time source-to-target reenactment approach for complete human portrait videos that enables transfer of torso and head motion, face expression, and eye gaze, is proposed.
Journal ArticleDOI
Interactive motion mapping for real-time character control
Helge Rhodin,James Tompkin,Kwang In Kim,Kwang In Kim,Kiran Varanasi,Hans-Peter Seidel,Christian Theobalt +6 more
TL;DR: This work uses interactively‐defined sparse pose correspondences to learn a mapping between arbitrary 3D point source sequences and mesh target sequences and puppet the target character in real time, which provides new ways to control characters for real‐time animation.
Patent
High dynamic range and tone mapping imaging techniques
Miguel Granados,Jose Rafael Tena,Tunc Ozan Aydin,Jean-François Lalonde,Christian Theobalt,Iain Matthews +5 more
TL;DR: In this article, a contrast waste score and a contrast loss score are calculated for a first tone-mapped image produced by the TMO, which can be used to optimize the performance of TMO by reducing noise and improving contrast.