Shared Latent Representation for Joint Text-to-Audio-Visual Synthesis

• URL: http://arxiv.org/abs/2511.05432v1
• Date: Fri, 07 Nov 2025 17:07:56 GMT
• Title: Shared Latent Representation for Joint Text-to-Audio-Visual Synthesis
• Authors: Dogucan Yaman, Seymanur Akti, Fevziye Irem Eyiokur, Alexander Waibel,
• Abstract summary: A Text-to-Vec module generates Wav2Vec2 embeddings from text, which jointly condition speech and face generation.<n>We adopt a two-stage training: pretraining on Wav2Vec2 embeddings and finetuning on TTS outputs.<n>Experiments show that conditioning on TTS-predicted latent features outperforms cascaded pipelines.
• Score: 57.5830191022097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a text-to-talking-face synthesis framework leveraging latent speech representations from HierSpeech++. A Text-to-Vec module generates Wav2Vec2 embeddings from text, which jointly condition speech and face generation. To handle distribution shifts between clean and TTS-predicted features, we adopt a two-stage training: pretraining on Wav2Vec2 embeddings and finetuning on TTS outputs. This enables tight audio-visual alignment, preserves speaker identity, and produces natural, expressive speech and synchronized facial motion without ground-truth audio at inference. Experiments show that conditioning on TTS-predicted latent features outperforms cascaded pipelines, improving both lip-sync and visual realism.

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