MoCA-Video: Motion-Aware Concept Alignment for Consistent Video Editing

Abstract:Unlike traditional video editing or inpainting, video semantic mixing fuses a reference concept with a moving target entity to produce a hybrid while preserving the source video's motion and layout. We propose MoCA-Video, a training-free framework that steers a frozen video-diffusion denoising trajectory through concept-localized reference injection. At selected low-noise steps, MoCA-Video uses concept attention to localize the target object and injects the reference latent into the localized region, where object structure has formed but appearance remains editable. A momentum-based correction carries the injected prediction across frames to encourage coherent concept integration through the sequence. We further introduce CASS, a CLIP-based metric that measures the output's directional alignment shift toward the reference and away from the source prompt. Using the denoiser's internal attention avoids an external localization model; in our A100 FP16 setup, MoCA-Video takes 3.2 seconds per output frame, excluding preprocessing. Across the evaluated baselines, MoCA-Video achieves the highest CASS, rel-CASS, and ImageReward, while LPIPS-T and FVD expose separate temporal-coherence and video-quality trade-offs.

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2506.01004 [cs.CV]

(or arXiv:2506.01004v3 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2506.01004 Focus to learn more arXiv-issued DOI via DataCite

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