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arXiv · 2512.05126

SyncVoice: Towards Video Dubbing with Vision-Augmented Pretrained TTS Model

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

Video dubbing aims to generate high-fidelity speech that is precisely temporally aligned with the visual content. Existing methods still suffer from limitations in speech naturalness and audio-visual synchronization, and are limited to monolingual settings. To address these challenges, we propose SyncVoice, a vision-augmented video dubbing framework built upon a pretrained text-to-speech (TTS) model. By fine-tuning the TTS model on audio-visual data, we achieve strong audiovisual consistency. We propose a Dual Speaker Encoder to effectively mitigate inter-language interference in cross-lingual speech synthesis and explore the application of video dubbing in video translation scenarios. Experimental results show that SyncVoice achieves high-fidelity speech generation with strong synchronization performance, demonstrating its potential in video dubbing tasks.

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Kaidi Wang, Yi He, Wenhao Guan, Weijie Wu, Hongwu Ding, Xiong Zhang, Di Wu, Meng Meng, Jian Luan, Lin Li, Qingyang Hong. 2025-11-23. SyncVoice: Towards Video Dubbing with Vision-Augmented Pretrained TTS Model. https://arxiv.org/abs/2512.05126

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