TokenHMR: Advancing Human Mesh Recovery with a Tokenized Pose Representation

Meshcapade 3 ETH Zurich Figure 1 . Existing methods that regress 3D human pose and shape (HPS) from an image (like HMR2.0 [12] ) estimate bodies that are either image-aligned or have accurate 3D pose, but not both. We show that this is a fundamental trade-off for existing methods. To address this our method, TokenHMR, introduces a novel loss, Threshold-Adaptive Loss Scaling (TALS), and a discrete token-based pose representation of 3D pose. With these, TokenHMR achieves state-of-the-art accuracy on multiple in-the-wild 3D benchmarks.