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。

GATOR: 面向图形的变形器和运动解耦回归的人体网格恢复从2D姿势