MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation

• URL: http://arxiv.org/abs/2212.09478v2
• Date: Fri, 24 Mar 2023 12:13:52 GMT
• Title: MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation
• Authors: Ludan Ruan and Yiyang Ma and Huan Yang and Huiguo He and Bei Liu and Jianlong Fu and Nicholas Jing Yuan and Qin Jin and Baining Guo
• Abstract summary: We propose the first joint audio-video generation framework that brings engaging watching and listening experiences simultaneously. MM-Diffusion consists of a sequential multi-modal U-Net for a joint denoising process by design. Experiments show superior results in unconditional audio-video generation, and zero-shot conditional tasks.
• Score: 70.74377373885645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose the first joint audio-video generation framework that brings engaging watching and listening experiences simultaneously, towards high-quality realistic videos. To generate joint audio-video pairs, we propose a novel Multi-Modal Diffusion model (i.e., MM-Diffusion), with two-coupled denoising autoencoders. In contrast to existing single-modal diffusion models, MM-Diffusion consists of a sequential multi-modal U-Net for a joint denoising process by design. Two subnets for audio and video learn to gradually generate aligned audio-video pairs from Gaussian noises. To ensure semantic consistency across modalities, we propose a novel random-shift based attention block bridging over the two subnets, which enables efficient cross-modal alignment, and thus reinforces the audio-video fidelity for each other. Extensive experiments show superior results in unconditional audio-video generation, and zero-shot conditional tasks (e.g., video-to-audio). In particular, we achieve the best FVD and FAD on Landscape and AIST++ dancing datasets. Turing tests of 10k votes further demonstrate dominant preferences for our model. The code and pre-trained models can be downloaded at https://github.com/researchmm/MM-Diffusion.

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