  Multi-person 3D pose estimation is a challenging task because of occlusion
and depth ambiguity, especially in the cases of crowd scenes. To solve these
problems, most existing methods explore modeling body context cues by enhancing
feature representation with graph neural networks or adding structural
constraints. However, these methods are not robust for their single-root
formulation that decoding 3D poses from a root node with a pre-defined graph.
In this paper, we propose GR-M3D, which models the \textbf{M}ulti-person
\textbf{3D} pose estimation with dynamic \textbf{G}raph \textbf{R}easoning. The
decoding graph in GR-M3D is predicted instead of pre-defined. In particular, It
firstly generates several data maps and enhances them with a scale and depth
aware refinement module (SDAR). Then multiple root keypoints and dense decoding
paths for each person are estimated from these data maps. Based on them,
dynamic decoding graphs are built by assigning path weights to the decoding
paths, while the path weights are inferred from those enhanced data maps. And
this process is named dynamic graph reasoning (DGR). Finally, the 3D poses are
decoded according to dynamic decoding graphs for each detected person. GR-M3D
can adjust the structure of the decoding graph implicitly by adopting soft path
weights according to input data, which makes the decoding graphs be adaptive to
different input persons to the best extent and more capable of handling
occlusion and depth ambiguity than previous methods. We empirically show that
the proposed bottom-up approach even outperforms top-down methods and achieves
state-of-the-art results on three 3D pose datasets.
