Speech Driven Video Editing via an Audio-Conditioned Diffusion Model

作者：Dan Bigioi, Shubhajit Basak, H. Jordan, R. Mcdonnell, Peter Corcoran · 发表于：Image and Vision Computing · 年份：2023 · DOI：10.48550/arXiv.2301.04474 · 被引用次数：48 · 研究领域：Computer Science、Engineering

Taking inspiration from recent developments in visual generative tasks using diffusion models, we propose a method for end-to-end speech-driven video editing using a denoising diffusion model. Given a video of a talking person, and a separate auditory speech recording, the lip and jaw motions are re-synchronized 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 on audio mel spectral features to generate synchronised facial motion. Proof of concept results are demonstrated on both single-speaker and multi-speaker video editing, providing a baseline model on the CREMA-D audiovisual data set. To the best of our knowledge, this is the first work to demonstrate and validate the feasibility of applying end-to-end denoising diffusion models to the task of audio-driven video editing.