RASA: Disentangled Spatial-Motional Priors for Cross-Identity Character Animation
Computer Vision and Pattern Recognition
Summary
The authors present RASA, a method to animate a new character so it mimics the movements of another video while keeping correct size and position. They separate the problem into two parts: first, aligning the new character’s shape and position with the moving character using a Spatial Prior Calibrator, and second, controlling realistic joint movements through an Inherent Motional Guider. They test their method on a new benchmark called CIM-Bench and show it works better than previous techniques for realistic and well-aligned animations. This work highlights the importance of handling spatial alignment and motion separately for better character animation.
cross-identity animationspatial mappingmotion controlDiffusion TransformerSMPL modelpose estimationjoint articulationvolumetric refinementcharacter animationbenchmark dataset
Authors
Zhen Xiao, Zhen Shen, Zhaofan Qiu, Ting Yao, Xueliang Liu, Tao Mei
Abstract
Cross-identity character animation aims to drive a target identity from a reference image to follow the motion of a source character from a driving video. The core challenge lies in the inherent entanglement of two capabilities: cross-identity spatial mapping (aligning position, scale, and skeletal proportions) and motion control (refining joint articulation, volumetric consistency, and view coherence). We introduce Reference-Aware Structural Alignment (RASA), a framework that disentangles spatial mapping from motion control by injecting structured priors into a Diffusion Transformer (DiT). Our approach has two stages. First, a Spatial Prior Calibrator (SPC) fuses reference identity with driving pose to generate a spatially grounded initial noise latent, ensuring correct positioning, scaling, and alignment with the driving skeleton. Second, an Inherent Motional Guider (IMG) encodes shape-agnostic SMPL articulation parameters into a semantic motion vector beyond appearance-biased 2D keypoints. Injected into intermediate DiT layers, this vector complements the base pose condition for anatomically consistent articulation and view-aware volumetric refinement. We curate CIM-Bench, a high-quality benchmark with rigorous curation, for evaluation. Extensive experiments show RASA significantly outperforms state-of-the-art methods in motion fidelity and visual quality. Our work establishes a new paradigm showing disentangled spatial and motional priors are key to robust character animation. Project page: https://hidream.ai.github.io/RASA/