twist-and-swing decomposition

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.