MorphGS: Morphology-Adaptive Articulated Motion Transfer from Videos

Transferring articulated motion from monocular videos torigged 3D characters is challenging due to pose ambiguity in 2D obser-vations and morphological differences between source and target. Ex-isting approaches often follow a reconstruct-then-retarget paradigm, ty-ing transfer quality to intermediate 3D reconstruction and limiting ap-plicability to categories with parametric templates. We propose Mor-phGS, a framework that formulates motion retargeting as a target-drivenanalysis-by-synthesis problem, directly optimizing target morphology andpose through image-space supervision. A rig-coupled morphology pa-rameterization factorizes character identity from time-varying joint rota-tions, while dense 2D-3D correspondences and synthesized views providecomplementary structural and multi-view guidance. Experiments on syn-thetic benchmarks and real-world videos show consistent improvementsover baselines. Project page: https://xodus777.github.io/MorphGS/