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arXiv · 2506.01004

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

Tong Zhang·Juan C Leon Alcazar·Victor Escorcia·Bernard Ghanem
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Abstract

We present MoCA-Video, a training-free framework for semantic mixing in videos. Operating in the latent space of a frozen video diffusion model, MoCA-Video utilizes class-agnostic segmentation with diagonal denoising scheduler to localize and track the target object across frames. To ensure temporal stability under semantic shifts, we introduce momentum-based correction to approximate novel hybrid distributions beyond trained data distribution, alongside a light gamma residual module that smooths out visual artifacts. We evaluate model's performance using SSIM, LPIPS, and a proposed metric, \metricnameabbr, which quantifies semantic alignment between reference and output. Extensive evaluation demonstrates that our model consistently outperforms both training-free and trained baselines, achieving superior semantic mixing and temporal coherence without retraining. Results establish that structured manipulation of diffusion noise trajectories enables controllable and high-quality video editing under semantic shifts.

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Tong Zhang, Juan C Leon Alcazar, Victor Escorcia, Bernard Ghanem. 2025-06-01. MoCA-Video: Motion-Aware Concept Alignment for Consistent Video Editing. https://arxiv.org/abs/2506.01004

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