Optical Flow-based 3D Human Motion Estimation from Monocular Video

This paper presents a method to estimate 3D human pose and body shape from monocular videos. While recent approaches infer the 3D pose from silhouettes and landmarks, we exploit properties of optical flow to temporally constrain the reconstructed motion. We estimate human motion by minimizing the difference between computed flow fields and the output of our novel flow renderer. By just using a single semi-automatic initialization step, we are able to reconstruct monocular sequences without joint annotation. Our test scenarios demonstrate that optical flow effectively regularizes the under-constrained problem of human shape and motion estimation from monocular video.
Author(s): | Thiemo Alldieck, Marc Kassubeck, Bastian Wandt, Bodo Rosenhahn, Marcus Magnor |
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Published: | September 2017 |
Type: | Article in conference proceedings |
Book: | Proc. German Conference on Pattern Recognition (GCPR) (Springer) |
ISBN: | 978-3-319-66709-6 |
Presented at: | German Conference on Pattern Recognition (GCPR) 2017 |
Project(s): | Comprehensive Human Performance Capture from Monocular Video Footage Immersive Digital Reality |
@inproceedings{alldieck2017optical, title = {Optical Flow-based 3D Human Motion Estimation from Monocular Video}, author = {Alldieck, Thiemo and Kassubeck, Marc and Wandt, Bastian and Rosenhahn, Bodo and Magnor, Marcus}, booktitle = {Proc. German Conference on Pattern Recognition ({GCPR})}, isbn = {978-3-319-66709-6}, editor = {Roth, Volker and Vetter, Thomas}, pages = {347--360}, month = {Sep}, year = {2017} }
Authors
Thiemo Alldieck
Fmr. ResearcherMarc Kassubeck
Fmr. ResearcherBastian Wandt
ExternalBodo Rosenhahn
ExternalMarcus Magnor
Director, Chair