TaoMate-FVSR:Audio-Guided Facial Video Restoration
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabThe paper presents TaoMate-FVSR, an audio-guided facial video restoration framework built on a video diffusion transformer. It mixes input video latents with noise to keep useful source information while regenerating corrupted mouth details, uses audio to guide lip movements, reference images to constrain appearance, and previously generated frames for temporal consistency. Training runs in two stages: visual restoration on degraded images and videos, then audio-guided lip correction on degraded dubbed videos. The model supports restoration with original audio and visual dubbing with replacement audio.
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The paper presents TaoMate-FVSR, an audio-guided facial video restoration framework built on a video diffusion transformer. It mixes input video latents with noise to keep useful source information while regenerating corrupted mouth details, uses audio to guide lip movements, reference images to constrain appearance, and previously generated frames for temporal consistency. Training runs in two stages: visual restoration on degraded images and videos, then audio-guided lip correction on degraded dubbed videos. The model supports restoration with original audio and visual dubbing with replacement audio.
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Key findings from the stored brief
- Source-noise mixing lets the model retain useful source information while regenerating corrupted mouth-region details.
- The method reports the lowest LPIPS and DISTS and the best lip synchronization scores among evaluated restoration baselines on talking-head benchmarks.
- Qualitative results on AI-generated videos show clearer tooth boundaries and lip contours than the compared approaches.
- The model supports both restoration with the original audio and visual dubbing with replacement audio.
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Why this may matter
AI-generated talking-head videos often show implausible teeth and lip motion that does not match speech. A method that repairs mouth regions while keeping identity and audio alignment addresses a specific failure mode in generated video.
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