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arXiv · 2506.12520

Good Noise Makes Good Edits: A Training-Free Diffusion-Based Video Editing with Image and Text Prompts

Saemee Choi·Sohyun Jeong·Hyojin Jang·Jaegul Choo·Jinhee Kim
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Abstract

We propose VINO, the first zero-shot, training-free video editing method conditioned on both image and text. Our approach introduces $\rho$-start sampling and dilated dual masking to construct structured noise maps that enable coherent and accurate edits. To further enhance visual fidelity, we present zero image guidance, a controllable negative prompt strategy. Extensive experiments demonstrate that VINO faithfully incorporates the reference image into video edits, achieving strong performance compared to state-of-the-art baselines, all without any test-time or instance-specific training.

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Saemee Choi, Sohyun Jeong, Hyojin Jang, Jaegul Choo, Jinhee Kim. 2025-06-14. Good Noise Makes Good Edits: A Training-Free Diffusion-Based Video Editing with Image and Text Prompts. https://arxiv.org/abs/2506.12520

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