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Carsten Stoll
Researcher at Facebook
Publications - 44
Citations - 3887
Carsten Stoll is an academic researcher from Facebook. The author has contributed to research in topics: Motion capture & Rendering (computer graphics). The author has an hindex of 25, co-authored 42 publications receiving 3505 citations. Previous affiliations of Carsten Stoll include Technische Universität Darmstadt & Max Planck Society.
Papers
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Journal ArticleDOI
Performance capture from sparse multi-view video
Edilson de Aguiar,Carsten Stoll,Christian Theobalt,Naveed Ahmed,Hans-Peter Seidel,Sebastian Thrun +5 more
TL;DR: A new marker-less approach to capturing human performances from multi-view video that can jointly reconstruct spatio-temporally coherent geometry, motion and textural surface appearance of actors that perform complex and rapid moves is proposed.
Journal ArticleDOI
A Statistical Model of Human Pose and Body Shape
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.
Proceedings ArticleDOI
Fast articulated motion tracking using a sums of Gaussians body model
TL;DR: A novel continuous and differentiable model-to-image similarity measure is introduced that can be used to estimate the skeletal motion of a human at 5–15 frames per second even for many camera views.