RASA: Disentangled Spatial-Motional Priors for Cross-Identity Character Animation

Cross-identity character animation aims to drive a targetidentity (from a reference image) to follow the motion of a source char-acter (from a driving video). The core challenge lies in the inherent en-tanglement of two essential capabilities: cross-identity spatial mapping-aligning position, scale, and skeletal proportions between the referenceand the driving pose-and subsequent motion control-refining joint articu-lation, volumetric consistency, and view coherence during generation. Inthis paper, we introduce Reference-Aware Structural Alignment(RASA), a novel framework that systematically disentangles spatialmapping from motion control by injecting structured priors into a Diffu-sion Transformer (DiT). Our approach operates in two complementarystages. First, a Spatial Prior Calibrator (SPC) fuses the referenceidentity with the driving pose to generate an initial noise latent thatis spatially grounded—it ensures the target character is correctly po-sitioned, scaled, and proportionally aligned with the driving skeleton.This resolves cross-identity spatial mismatches at the very start of gen-eration. Second, to achieve identity-agnostic motion control, we proposean Inherent Motional Guider (IMG). Moving beyond appearance-biased 2D keypoints, IMG encodes shape-agnostic SMPL articulationparameters into a semantic motion vector. Injected into the interme-diate layers of the DiT, this vector serves as complementary guidancethat works in tandem with the base pose condition, providing anatomi-cally consistent joint articulation and view-aware volumetric refinement.To rigorously evaluate this challenging task, we curate CIM-Bench, ahigh-quality benchmark with rigorous manual curation. Extensive exper-iments demonstrate that RASA significantly outperforms state-of-the-artmethods in both motion fidelity and visual quality. Our work establishesa new paradigm for cross-identity animation, showing that disentan-gled spatial and motional priors are key to achieving robust and con-sistent character animation. Our project page is at https://hidream-ai.github.io/RASA/.