  Estimating human poses from videos is critical in human-computer interaction.
Joints cooperate rather than move independently during human movement. There
are both spatial and temporal correlations between joints. Despite the positive
results of previous approaches, most focus on modeling the spatial correlation
between joints while only straightforwardly integrating features along the
temporal dimension, ignoring the temporal correlation between joints. In this
work, we propose a plug-and-play kinematics modeling module (KMM) to explicitly
model temporal correlations between joints across different frames by
calculating their temporal similarity. In this way, KMM can capture motion cues
of the current joint relative to all joints in different time. Besides, we
formulate video-based human pose estimation as a Markov Decision Process and
design a novel kinematics modeling network (KIMNet) to simulate the Markov
Chain, allowing KIMNet to locate joints recursively. Our approach achieves
state-of-the-art results on two challenging benchmarks. In particular, KIMNet
shows robustness to the occlusion. The code will be released at
https://github.com/YHDang/KIMNet.
