SMPL-IKS: An Inverse Kinematic Solver for 3D Human Mesh Recovery

We present SMPL-IKS, an inverse kinematic solver to operate on the well-known Skinned Multi-Person Linear model (SMPL) to recover human mesh from 3D skeleton. The challenges of the task are threefold: (1) Shape Mismatching. (2) Error Accumulation. (3) Rotation Ambiguity. Instead of recovering human mesh from costly vertice up-sampling or iterative optimization as in previous methods, SMPL-IKS directly regresses the SMPL parameters (i.e., shape and pose parameters) in a clean and efficient way. Specifically, we propose to infer skeleton-to-mesh via two explicit mappings viz. SI and IK, the former maps bone length to shape parameters, and the latter maps bone direction to pose parameters. Moreover, we design two adaptive pose refinement mechanisms, termed PR, to alleviate the error accumulation problem. SMPL-IKS is general and thus extensible to MANO or SMPL-H. Extensive experiments are conducted on various benchmarks for body-only, hand-only, and body-hand scenarios. Our model surpasses the state-of-the-art methods by a large margin while being much more efficient. Data and code is available at https://github.com/Z-Z-J/SMPL-IKS