SA-V2V: Training-Free Subject-Aware Video-to-Video Personalization

Video personalization aims to generate personalized videos by preserving the motion and background of a reference video while transferring the subject appearance from a given subject image. Such video personalization has achieved tremendous progress alongside recent advancements in video diffusion transformers (DiT). Existing DiT-based approaches predominantly rely on training-based methods that require auxiliary control signals, while independent control over motion, subject appearance, and background remains challenging due to the entangled nature of the unified spatiotemporal attention mechanism. In this paper, we uncover a key structural insight that the spatio-temporal attention matrix of DiT exhibits an inherent functional decomposition—intra-frame blocks primarily encode spatial information, while inter-frame blocks encode motion dynamics. Building on this observation, we introduce SA-V2V, a novel training-free video personalization framework that disentangles and enables independent control over these visual attributes. We propose two guidance approaches: (1) For motion guidance, we present stochastic temporal injection, which leverages inter-frame attention maps to precisely control dynamic trajectories.(2) For appearance guidance, we propose a Target-aware Feature Guidance to enable independent control over subject and background appearance by modulating the synthesized features with masked attention features from the subject image and the reference video, respectively. Extensive experiments demonstrate that the proposed SA-V2V significantly outperforms existing approaches in motion fidelity, subject adherence, and background preservation. Our code is available at https://parksoobin1219.github.io/SA-V2V/