Co-Evolution of Pose and Mesh for 3D Human Body Estimation from Video
This paper proposes the Pose and Mesh Co-Evolution network (PMCE), a new two-stage pose-to-mesh framework for recovering 3D human mesh from a monocular video. PMCE frst estimates 3D human pose motion in terms of spatial and temporal domains, then performs image-guided pose and mesh interactions by our proposed AdaLN that injects body shape information while preserving their spatial structure. Extensive experiments on popular datasets show that PMCE outperforms state-of-the-art methods in both perframe accuracy and temporal consistency. We hope that our approach will spark further research in 3D human motion estimation considering both pose and shape consistency.
Oct 2, 2023
Interweaved Graph and Attention Network for 3D Human Pose Estimation
An Interweaved Graph and Attention Network (IGANet) for 3D human pose estimation that enables bidirectional communication between GCNs and attentions, capturing both global and local correlations in human skeleton representations.
Jun 4, 2023
Gator: Graph-Aware Transformer with Motion-Disentangled Regression for Human Mesh Recovery from a 2D Pose
A Graph-Aware Transformer (GATOR) framework for 3D human mesh recovery from 2D pose, combining Graph-Aware Transformer encoder and Motion-Disentangled Regression decoder to capture joint-joint, joint-vertex, and vertex-vertex relations.
Jun 4, 2023