Computer Graphics
TU Braunschweig

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


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
Year:2017
Month:September
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 


@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}
}

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