Motion-Aware Concept Alignment for Consistent Video Editing

• URL: http://arxiv.org/abs/2506.01004v1
• Date: Sun, 01 Jun 2025 13:28:04 GMT
• Title: Motion-Aware Concept Alignment for Consistent Video Editing
• Authors: Tong Zhang, Juan C Leon Alcazar, Bernard Ghanem,
• Abstract summary: We introduce MoCA-Video (Motion-Aware Concept Alignment in Video), a training-free framework bridging the gap between image-domain semantic mixing and video.<n>Given a generated video and a user-provided reference image, MoCA-Video injects the semantic features of the reference image into a specific object within the video.<n>We evaluate MoCA's performance using the standard SSIM, image-level LPIPS, temporal LPIPS, and introduce a novel metric CASS (Conceptual Alignment Shift Score) to evaluate the consistency and effectiveness of the visual shifts between the source prompt and the modified video frames
• Score: 57.08108545219043
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce MoCA-Video (Motion-Aware Concept Alignment in Video), a training-free framework bridging the gap between image-domain semantic mixing and video. Given a generated video and a user-provided reference image, MoCA-Video injects the semantic features of the reference image into a specific object within the video, while preserving the original motion and visual context. Our approach leverages a diagonal denoising schedule and class-agnostic segmentation to detect and track objects in the latent space and precisely control the spatial location of the blended objects. To ensure temporal coherence, we incorporate momentum-based semantic corrections and gamma residual noise stabilization for smooth frame transitions. We evaluate MoCA's performance using the standard SSIM, image-level LPIPS, temporal LPIPS, and introduce a novel metric CASS (Conceptual Alignment Shift Score) to evaluate the consistency and effectiveness of the visual shifts between the source prompt and the modified video frames. Using self-constructed dataset, MoCA-Video outperforms current baselines, achieving superior spatial consistency, coherent motion, and a significantly higher CASS score, despite having no training or fine-tuning. MoCA-Video demonstrates that structured manipulation in the diffusion noise trajectory allows for controllable, high-quality video synthesis.

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