Title: Motion-Aware Concept Alignment for Consistent Video Editing
Primary area: generative models
Keywords: Video editing, diffusion models, semantic mixing

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, MoCA-Video, 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.

TL;DR: MoCA-Video is a training-free framework that enables controllable, temporally consistent semantic video editing by manipulating diffusion noise trajectories.