SyncVoice: Simple and Effective Automatic Video Dubbing with Vision-Augmented TTS

Kaidi Wang; Yi He; Wenhao Guan; Weijie Wu; Peijie Chen; Hongwu Ding; Xiong Zhang; Di Wu; Meng Meng; Jian Luan; Lin Li; Qingyang Hong

arXiv · 2512.05126

SyncVoice: Simple and Effective Automatic Video Dubbing with Vision-Augmented TTS

Kaidi Wang·Yi He·Wenhao Guan·Weijie Wu·Peijie Chen·Hongwu Ding·Xiong Zhang·Di Wu·Meng Meng·Jian Luan·Lin Li·Qingyang Hong

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Abstract

Automatic video dubbing aims to generate high-fidelity speech that is temporally aligned with visual content. However, existing methods still suffer from limited speech naturalness, insufficient audio-visual synchronization, and poor scalability beyond monolingual settings. To address these challenges, we propose SyncVoice, a simple and effective dubbing framework that lightly integrates a Text-Visual Fusion Module into a pretrained text-to-speech (TTS) system. This module aligns visual features with linguistic representations, enabling temporally synchronized speech synthesis without complex architectural redesign. Experiments on the LRS3 dataset show that SyncVoice achieves state-of-the-art performance in zero-shot dubbing. Further training on a large-scale bilingual audio-visual dataset improves vocal fidelity while preserving synchronization, yielding a single unified model for both Chinese and English dubbing.

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BibTeX RIS

Kaidi Wang, Yi He, Wenhao Guan, Weijie Wu, Peijie Chen, Hongwu Ding, Xiong Zhang, Di Wu, Meng Meng, Jian Luan, Lin Li, Qingyang Hong. 2026-09-15. SyncVoice: Simple and Effective Automatic Video Dubbing with Vision-Augmented TTS. https://arxiv.org/abs/2512.05126

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