Speech Driven Video Editing via an Audio-Conditioned Diffusion Model
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In this paper we propose a method for end-to-end speech driven video editing using a denoising diffusion model. Given a video of a person speaking, we aim to re-synchronise the lip and jaw motion of the person in response to a separate auditory speech recording without relying on intermediate structural representations such as facial landmarks or a 3D face model. We show this is possible by conditioning a denoising diffusion model with audio spectral features to generate synchronised facial motion. We achieve convincing results on the task of unstructured single-speaker video editing, achieving a word error rate of 45 demonstrate how our approach can be extended to the multi-speaker domain. To our knowledge, this is the first work to explore the feasibility of applying denoising diffusion models to the task of audio-driven video editing.
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