  In this study, we aim 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 each single-modal model 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 theoretically show that this guidance can be computed
through the gradient of the optimal discriminator distinguishing real
audio-video pairs from fake ones independently generated by the base models. On
the basis of this analysis, we construct the joint guidance module by training
this discriminator. Additionally, we adopt a loss function to make the gradient
of the discriminator work as a noise estimator, as in standard diffusion
models, stabilizing the gradient of the discriminator. Empirical evaluations on
several benchmark datasets demonstrate that our method improves both
single-modal fidelity and multi-modal alignment with a relatively small number
of parameters.
