Smooth Motion Stitching via Laplacian Optimization in Rodrigues Vector Space

Researchers have introduced a motion stitching framework that treats joint rotation editing as a temporal Laplacian optimization problem in Rodrigues vector space. The practical goal is straightforward: blend animation segments more smoothly while preserving the characteristic timing and variation of the source motion, instead of flattening everything into a generic interpolation.

The key technical choice is representing rotations as continuous Rodrigues vectors. That makes the optimization more stable for stitching than naive linear interpolation, and it supports both intra-category replacement and cross-category motion stitching. In other words, it can swap or combine motions even when the source clips differ substantially, without leaning on a trained model or a pile of manual parameter tuning.

For game teams, the appeal is workflow and iteration speed. The method is described as computationally efficient enough for interactive editing, which matters for animation authoring tools, preview systems, and any pipeline where animators or technical artists need quick feedback. The experiments also suggest the transitions stay visually coherent across a wide range of motion differences.

The work also digs into rotational continuity, arguing that axis inversions are rare in real motion data and that the instability seen in synthetic axis-flip cases is not a practical blocker. That makes the approach feel less like a lab curiosity and more like a candidate for production-facing tools, especially where motion cleanup, retargeting, or clip stitching still depends on fragile...