3D human mesh recovery from a 2D pose plays an important role in various
applications. However, it is hard for existing methods to simultaneously
capture the multiple relations during the evolution from skeleton to mesh,
including joint-joint, joint-vertex and vertex-vertex relations, which often
leads to implausible results. To address this issue, we propose a novel
solution, called GATOR, that contains an encoder of Graph-Aware Transformer
(GAT) and a decoder with Motion-Disentangled Regression (MDR) to explore these
multiple relations. Specifically, GAT combines a GCN and a graph-aware
self-attention in parallel to capture physical and hidden joint-joint
relations. Furthermore, MDR models joint-vertex and vertex-vertex interactions
to explore joint and vertex relations. Based on the clustering characteristics
of vertex offset fields, MDR regresses the vertices by composing the predicted
base motions. Extensive experiments show that GATOR achieves state-of-the-art
performance on two challenging benchmarks.

研究提出了一种名为 GATOR 的新颖方法，其中包含了一个 AGT 编码器和一个具有 MDR 的解码器，以同时探索骨架到网格的多个关系，包括关节 - 关节、关节 - 顶点和顶点 - 顶点关系，并在顶点偏移场的聚类特征的基础上推断出顶点。