Motion capture and retargeting for character animation: A Survey

Abstract

Motion capture serves as the core data acquisition method for character animation, while motion retargeting enables the cross-character reuse of captured motion sequences. The integrated pipeline of capture and retargeting has become the mainstream production workflow for games, VR, and digital human creation. Traditional geometric retargeting methods are prone to foot sliding, ground penetration, and loss of contact semantics when processing motion capture data with occlusion noise, whereas recent deep learning and physical optimization algorithms have significantly improved the naturalness of retargeted motions. This paper systematically reviews representative literature on motion capture and retargeting, organized into five categories following the complete industrial workflow: multi-modal motion acquisition and preprocessing, cross-skeleton shape-adaptive retargeting, AI-assisted motion generation, physically constrained interactive retargeting, and end-to-end integrated pipelines with cross-domain applications. For each category, we elaborate on core technical schemes, inherent limitations, and typical experimental outcomes with standard citations. This survey provides systematic literature support for researchers engaged in motion capture and retargeting animation.