Audio-Visual Foundation Models, which are pretrained to jointly generate sound and visual content, have recently shown an unprecedented ability to model multi-modal generation and editing, opening new opportunities for downstream tasks. Among these tasks, video dubbing could greatly benefit from such priors, yet most existing solutions still rely on complex, task-specific pipelines that struggle in real-world settings. In this work, we introduce a single-model approach that adapts a foundational audio-video diffusion model for video-to-video dubbing via a lightweight LoRA. The LoRA enables the model to condition on an input audio-video while jointly generating translated audio and synchronized facial motion. To train this LoRA, we leverage the generative model itself to synthesize paired multilingual videos of the same speaker. Specifically, we generate multilingual videos with language switches within a single clip, and then inpaint the face and audio in each half to match the language of the other half. By leveraging the rich generative prior of the audio-visual model, our approach preserves speaker identity and lip synchronization while remaining robust to complex motion and real-world dynamics. We demonstrate that our approach produces high-quality dubbed videos with improved visual fidelity, lip synchronization, and robustness compared to existing dubbing pipelines.

翻译：音视频基础模型，通过预训练联合生成声音和视觉内容，近期展现了前所未有的多模态生成与编辑能力，为下游任务开辟了新机遇。其中，视频配音本可极大受益于此类先验知识，但现有解决方案仍多依赖复杂且特定任务的流程，难以应对真实场景。本文提出一种单模型方法，通过轻量级LoRA适配基础音视频扩散模型实现视频到视频的配音。该LoRA使模型能够基于输入音视频进行条件生成，同时输出翻译后的音频与同步的面部运动。为训练此LoRA，我们利用生成模型自身合成同一说话人的多语言配对视频。具体而言，我们生成包含语言切换的多语言视频片段，随后对每半段的面部与音频进行修复，使其语言与另一半相匹配。通过利用音视频模型的丰富生成先验，我们的方法在保持说话人身份和唇形同步的同时，对复杂运动及真实世界动态具有鲁棒性。实验表明，与现有配音流程相比，我们的方法能生成更高质量的视频配音，在视觉保真度、唇形同步及鲁棒性方面均有提升。