VidDub: Replacement-Aware Distillation for Two-Step Video Dubbing

Yexin Liu; Yunlong Lin; Zehan Wang; Guangxin He; Xinli Xu; Jiajun Zha; Yaofu Liu; Yueze Wang; Bowen Ping; Yuanming Li; Amie Wang; Bernard Ghanem; Bing Li; Harry Yang

arXiv · 2611.03404

VidDub: Replacement-Aware Distillation for Two-Step Video Dubbing

Yexin Liu·Yunlong Lin·Zehan Wang·Guangxin He·Xinli Xu·Jiajun Zha·Yaofu Liu·Yueze Wang·Bowen Ping·Yuanming Li·Amie Wang·Bernard Ghanem·Bing Li·Harry Yang

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Abstract

Video dubbing aims to synthesize lip movements that match target speech while preserving the speaker identity, head pose, and background of a source video. Existing editors are predominantly trained via self-reconstruction under original paired audio, even though the source mouth becomes a conflicting target when driving speech is replaced. We introduce replacement-aware cross-audio supervision: paired-audio samples use full-frame reconstruction, whereas unpaired replacement-audio samples mask out source-mouth reconstruction losses and supervise the mouth solely through audio-visual synchronization while preserving unmasked facial regions and background. Because offline dubbing provides the complete source video and target speech beforehand, we present VidDub, a full-context diffusion editor distilled into two denoising steps (2 NFE). Our replacement-aware teacher first learns accurate lip synchronization without regressing toward conflicting source mouth movements. Matched trajectory regression then transfers the teacher's CFG-guided trajectories to a two-step student, and distribution matching distillation recovers visual quality after step compression. We will release the curated training dataset, VidDub-Bench, fixed paired- and cross-audio evaluation manifests, preprocessing outputs, and unified evaluation code. Across three training seeds, masking out conflicting source-mouth reconstruction improves cross-audio Sync-C by 0.833; the final two-step model combines strong lip synchronization with superior visual quality, identity preservation, and visual speech recognition across four benchmarks.

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Yexin Liu, Yunlong Lin, Zehan Wang, Guangxin He, Xinli Xu, Jiajun Zha, Yaofu Liu, Yueze Wang, Bowen Ping, Yuanming Li, Amie Wang, Bernard Ghanem, Bing Li, Harry Yang. 2026-11-02. VidDub: Replacement-Aware Distillation for Two-Step Video Dubbing. https://arxiv.org/abs/2611.03404

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