Learning Visual Speech Representations via Cross-Modal Distillation and Joint Face-Lip Modeling
Lipreading remains challenging due to the inherent ambiguity of visual speech cues and their sensitivity to head pose and video resolution. In this work, we propose FLARE, a framework for robust visual speech representation learning via cross-modal distillation and joint face-lip modeling. FLARE leverages semantically rich Whisper representations as the acoustic teacher signal, converting them into discrete distillation targets through a random-projection quantizer and pretraining the model with a masked prediction objective. To capture complementary visual information, FLARE employs a dual-stream architecture that jointly processes lip region-of-interest (ROI) and full-face inputs, where...
| Authors | Jing-Xuan Zhang, Genshun Wan, Jianqing Gao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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