Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explicit Spatiotemporal Joint Relation Learning for Tracking Human Pose   We present a method for human pose tracking that is based on learning
spatiotemporal relationships among joints. Beyond generating the heatmap of a
joint in a given frame, our system also learns to predict the offset of the
joint from a neighboring joint in the frame. Additionally, it is trained to
predict the displacement of the joint from its position in the previous frame,
in a manner that can account for possibly changing joint appearance, unlike
optical flow. These relational cues in the spatial domain and temporal domain
are inferred in a robust manner by attending only to relevant areas in the
video frames. By explicitly learning and exploiting these joint relationships,
our system achieves state-of-the-art performance on standard benchmarks for
various pose tracking tasks including 3D body pose tracking in RGB video, 3D
hand pose tracking in depth sequences, and 3D hand gesture tracking in RGB
video.

18
The categories of this paper are: econ
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con