Smooth Motion Stitching via Laplacian Optimization in Rodrigues Vector Space
This work addresses the challenges of unnatural transitions and poor preservation of temporal motion characteristics in motion clip stitching by proposing a learning-free, parameter-free optimization method based on Rodrigues vectors. By representing joint rotations as continuous Rodrigues vectors and formulating the stitching process as a Laplacian smoothing problem in the time domain, the approach effectively enforces rotational continuity and numerical stability—leveraging the observation that rotation axis flips are rare in real human motion. The method supports both intra-class replacement and cross-category motion stitching, producing visually coherent and temporally faithful transitions even between highly dissimilar motions, while enabling efficient interactive editing.