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

• URL: http://arxiv.org/abs/2507.19225v1
• Date: Fri, 25 Jul 2025 12:49:06 GMT
• Title: Face2VoiceSync: Lightweight Face-Voice Consistency for Text-Driven Talking Face Generation
• Authors: Fang Kang, Yin Cao, Haoyu Chen,
• Abstract summary: Given a face image and text to speak, we generate talking face animation and its corresponding speeches.<n>We propose a novel framework, Face2VoiceSync, with several novel contributions.<n> Experiments show Face2VoiceSync achieves both visual and audio state-of-the-art performances on a single 40GB GPU.
• Score: 14.036076647627553
• License: http://creativecommons.org/licenses/by/4.0/
• 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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