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3D human mesh recovery from a 2D pose plays an important role in variousapplications. However, it is hard for existing methods to simultaneouslycapture the multiple relations during the evolution from skeleton to mesh,including joint-joint, joint-vertex and vertex-vertex relations, which oftenleads to implausible results. To address this issue, we propose a novelsolution, called GATOR, that contains an encoder of Graph-Aware Transformer(GAT) and a decoder with Motion-Disentangled Regression (MDR) to explore thesemultiple relations. Specifically, GAT combines a GCN and a graph-awareself-attention in parallel to capture physical and hidden joint-jointrelations. Furthermore, MDR models joint-vertex and vertex-vertex interactionsto explore joint and vertex relations. Based on the clustering characteristicsof vertex offset fields, MDR regresses the vertices by composing the predictedbase motions. Extensive experiments show that GATOR achieves state-of-the-artperformance on two challenging benchmarks.

GATOR: Graph-Aware Transformer with Motion-Disentangled Regression for Human Mesh Recovery from a 2D Pose