TY - GEN

T1 - Weakly-supervised Learning of Human Dynamics

AU - Zell, Petrissa

AU - Rosenhahn, Bodo

AU - Wandt, Bastian

N1 - Funding information: Acknowledgement. Research supported by the European Research Council (ERC-2013-PoC). The authors would like to thank all subjects who participated in data acquisition.

PY - 2020

Y1 - 2020

N2 - This paper proposes a weakly-supervised learning framework for dynamics estimation from human motion. Although there are many solutions to capture pure human motion readily available, their data is not sufficient to analyze quality and efficiency of movements. Instead, the forces and moments driving human motion (the dynamics) need to be considered. Since recording dynamics is a laborious task that requires expensive sensors and complex, time-consuming optimization, dynamics data sets are small compared to human motion data sets and are rarely made public. The proposed approach takes advantage of easily obtainable motion data which enables weakly-supervised learning on small dynamics sets and weakly-supervised domain transfer. Our method includes novel neural network (NN) layers for forward and inverse dynamics during end-to-end training. On this basis, a cyclic loss between pure motion data can be minimized, i.e. no ground truth forces and moments are required during training. The proposed method achieves state-of-the-art results in terms of ground reaction force, ground reaction moment and joint torque regression and is able to maintain good performance on substantially reduced sets.

AB - This paper proposes a weakly-supervised learning framework for dynamics estimation from human motion. Although there are many solutions to capture pure human motion readily available, their data is not sufficient to analyze quality and efficiency of movements. Instead, the forces and moments driving human motion (the dynamics) need to be considered. Since recording dynamics is a laborious task that requires expensive sensors and complex, time-consuming optimization, dynamics data sets are small compared to human motion data sets and are rarely made public. The proposed approach takes advantage of easily obtainable motion data which enables weakly-supervised learning on small dynamics sets and weakly-supervised domain transfer. Our method includes novel neural network (NN) layers for forward and inverse dynamics during end-to-end training. On this basis, a cyclic loss between pure motion data can be minimized, i.e. no ground truth forces and moments are required during training. The proposed method achieves state-of-the-art results in terms of ground reaction force, ground reaction moment and joint torque regression and is able to maintain good performance on substantially reduced sets.

KW - cs.CV

KW - Forward dynamics

KW - Human motion

KW - Weakly-supervised learning

KW - Inverse dynamics

KW - Artificial neural networks

KW - Domain transfer

UR - http://www.scopus.com/inward/record.url?scp=85097274716&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-58574-7_5

DO - 10.1007/978-3-030-58574-7_5

M3 - Conference contribution

SN - 978-3-030-58573-0

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 68

EP - 84

BT - Computer Vision – ECCV 2020

A2 - Vedaldi, Andrea

A2 - Bischof, Horst

A2 - Brox, Thomas

A2 - Frahm, Jan-Michael

ER -