  Graph Convolution Network (GCN) has been successfully used for 3D human pose
estimation in videos. However, it is often built on the fixed human-joint
affinity, according to human skeleton. This may reduce adaptation capacity of
GCN to tackle complex spatio-temporal pose variations in videos. To alleviate
this problem, we propose a novel Dynamical Graph Network (DG-Net), which can
dynamically identify human-joint affinity, and estimate 3D pose by adaptively
learning spatial/temporal joint relations from videos. Different from
traditional graph convolution, we introduce Dynamical Spatial/Temporal Graph
convolution (DSG/DTG) to discover spatial/temporal human-joint affinity for
each video exemplar, depending on spatial distance/temporal movement similarity
between human joints in this video. Hence, they can effectively understand
which joints are spatially closer and/or have consistent motion, for reducing
depth ambiguity and/or motion uncertainty when lifting 2D pose to 3D pose. We
conduct extensive experiments on three popular benchmarks, e.g., Human3.6M,
HumanEva-I, and MPI-INF-3DHP, where DG-Net outperforms a number of recent SOTA
approaches with fewer input frames and model size.
