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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MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
Ayush Tewari,Michael Zollhöfer,Hyeongwoo Kim,Pablo Garrido,Florian Bernard,Patrick Pérez,Christian Theobalt +6 more
TL;DR: A novel model-based deep convolutional autoencoder that addresses the highly challenging problem of reconstructing a 3D human face from a single in-the-wild color image and can be trained end-to-end in an unsupervised manner, which renders training on very large real world data feasible.
Motion Capture Using Joint Skeleton Tracking and Surface Estimation
Juergen Gall,Carsten Stoll,Edilson de Aguiar,Christian Theobalt,Bodo Rosenhahn,Hans-Peter Seidel +5 more
TL;DR: This paper proposes a method for capturing the performance of a human or an animal from a multi-view video sequence and proposes a novel optimization scheme for skeleton-based pose estimation that exploits the skeleton's tree structure to split the optimization problem into a local one and a lower dimensional global one.
Proceedings ArticleDOI
Motion capture using joint skeleton tracking and surface estimation
Juergen Gall,Carsten Stoll,Edilson de Aguiar,Christian Theobalt,Bodo Rosenhahn,Hans-Peter Seidel +5 more
TL;DR: This paper proposes a method for capturing the performance of a human or an animal from a multi-view video sequence and proposes a novel optimization scheme for skeleton-based pose estimation that exploits the skeleton's tree structure to split the optimization problem into a local one and a lower dimensional global one.
Book ChapterDOI
VolumeDeform: Real-Time Volumetric Non-rigid Reconstruction
TL;DR: In this paper, a single hand-held consumer-grade RGB-D sensor at real-time rates is used to reconstruct dynamic geometric shapes using a set of sparse color features in combination with a dense depth constraint.
A Noise‐aware Filter for Real‐time Depth Upsampling
TL;DR: This work presents an adaptive multi-lateral upsampling filter that takes into account the inherent noisy nature of real-time depth data and can greatly improve reconstruction quality, boost the resolution of the data to that of the video sensor, and prevent unwanted artifacts like texture copy into geometry.