Motion Retargeting Based on Inter-Character 3D Gaussian Mapping Pairs

Motion retargeting aims to model the correlation between source and target characters and transfer the source motion to the target character. Conventional deep learning methods typically take the source and target meshes as a condition to directly generate target motion. These methods require large numbers of training samples, and their retargeting performance drops substantially when the two characters differ greatly in skeleton or morphology. To address these problems, we propose a motion retargeting method based on inter-character 3D Gaussian Mapping Pairs (GMPs). Our method transfers motion through the body-part primitives (BPPs) of the source and target characters. GMPs represent the mapping between their BPPs and enable source-to-target motion retargeting without requiring large numbers of training samples. We design an evolutionary framework consisting of GMP-Selection and GMP-Update to optimize GMPs. GMP-Selection uses a fitness function based on morphological and homeomorphic constraints to stabilize the search for GMPs. GMP-Update uses the same constraints to guide Gaussian adjustment, cloning, and splitting to accelerate GMP refinement. We also design a "Gaussian Binding”–"Motion Correction” strategy. It ensures motion fidelity, prevents interpenetration, and maintains continuity during motion retargeting by using tracking, collision, and smoothness constraints. Our method provides an interpretable inter-character representation for motion retargeting and supports characters with large differences. Extensive experiments on the public Mixamo dataset show that our method delivers leading performance.