MMDisCo: Multi-Modal Discriminator-Guided Cooperative Diffusion for Joint Audio and Video Generation
Akio Hayakawa Masato Ishii Takashi Shibuya Yuki Mitsufuji
This study aims to construct an audio-video generative model with minimal computational cost by leveraging pre-trained single-modal generative models for audio and video.
To achieve this, we propose a novel method that guides single-modal models to cooperatively generate well-aligned samples across modalities. 
Specifically, given two pre-trained base diffusion models, we train a lightweight joint guidance module to adjust scores separately estimated by the base models to match the score of joint distribution over audio and video. 
We show that this guidance can be computed using the gradient of the optimal discriminator, which distinguishes real audio-video pairs from fake ones independently generated by the base models. 
Based on this analysis, we construct a joint guidance module by training this discriminator.
Additionally, we adopt a loss function to stabilize the discriminator's gradient and make it work as a noise estimator, as in standard diffusion models. 
Empirical evaluations on several benchmark datasets demonstrate that our method improves both single-modal fidelity and multimodal alignment with relatively few parameters.
The code is available at: [https://github.com/SonyResearch/MMDisCo](https://github.com/SonyResearch/MMDisCo).
Diffusion models, Multi-modal data, Audio-visual generative models
Building audio-video joint generative model with minimal computational cost via multimodal discriminator on the top of pre-trained single-modal generative models