From Inpainting to Editing: A Self-Bootstrapping Framework for Context-Rich Visual Dubbing
In brief:
Audio-driven visual dubbing aims to synchronize a video's lip movements with new speech, but is fundamentally challenged by the lack of ideal training data: paired videos where only a subject's lip movements differ…
Audio-driven visual dubbing aims to synchronize a video's lip movements with new speech, but is fundamentally challenged by the lack of ideal training data: paired videos where only a subject's lip…
Existing methods circumvent this with a mask-based inpainting paradigm, where an incomplete visual conditioning forces models to simultaneously hallucinate missing content and sync lips, leading to…
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Source
arXiv cs.CV
Type
Research Preprint
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Peer-submitted research paper on arXiv
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arXiv cs.CV·Research Preprint·Primary Source·
From Inpainting to Editing: A Self-Bootstrapping Framework for Context-Rich Visual Dubbing
TL;DR
Audio-driven visual dubbing aims to synchronize a video's lip movements with new speech, but is fundamentally challenged by the lack of ideal training data: paired videos where only a subject's lip movements differ…
Scan abstract → experiments → limitations. Also: verify benchmark methodology; note model size and inference requirements.
Full Analysis
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Audio-driven visual dubbing aims to synchronize a video's lip movements with new speech, but is fundamentally challenged by the lack of ideal training data: paired videos where only a subject's lip…
Existing methods circumvent this with a mask-based inpainting paradigm, where an incomplete visual conditioning forces models to simultaneously hallucinate missing content and sync lips, leading to…
