Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weakly-supervised Learning of Human Dynamics   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.

15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci