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arXiv · 2507.19225

Face2VoiceSync: Lightweight Face-Voice Consistency for Text-Driven Talking Face Generation

Fang Kang·Yin Cao·Haoyu Chen
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Abstract

Recent studies in speech-driven talking face generation achieve promising results, but their reliance on fixed-driven speech limits further applications (e.g., face-voice mismatch). Thus, we extend the task to a more challenging setting: given a face image and text to speak, generating both talking face animation and its corresponding speeches. Accordingly, we propose a novel framework, Face2VoiceSync, with several novel contributions: 1) Voice-Face Alignment, ensuring generated voices match facial appearance; 2) Diversity \& Manipulation, enabling generated voice control over paralinguistic features space; 3) Efficient Training, using a lightweight VAE to bridge visual and audio large-pretrained models, with significantly fewer trainable parameters than existing methods; 4) New Evaluation Metric, fairly assessing the diversity and identity consistency. Experiments show Face2VoiceSync achieves both visual and audio state-of-the-art performances on a single 40GB GPU.

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BibTeXRIS

Fang Kang, Yin Cao, Haoyu Chen. 2025-07-25. Face2VoiceSync: Lightweight Face-Voice Consistency for Text-Driven Talking Face Generation. https://arxiv.org/abs/2507.19225

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