Diffusion models improve video background music by aligning representations
Self-Representation Alignment in Diffusion Models for Video Background Music Generation
Artificial Intelligence
Summary
Generating music that matches video scenes is difficult because the internal understanding of diffusion models can be weak during early steps of creation. The authors proposed a method to help these models better learn by aligning their early and later internal views during training. This helps the model produce higher-quality music that fits with the video. They also use a way to focus the music on parts of the video for better synchronization. Tests showed their approach outperformed previous methods in matching music to videos and improving music quality.
What this means in practice
- •For video game developers: Create adaptive background music that closely matches game scenes using improved diffusion models aligned for better music synthesis.
- •For video content producers: Generate high-quality, synchronized background music for videos automatically by using a music generation model tailored to video features.$Commercial implications: Enables selling AI tools that automatically compose fitting background music for video producers, enhancing productivity and content appeal.
Authors
Jincheng Zhang, György Fazekas, Charalampos Saitis
Abstract
Diffusion models have been widely used for various generative tasks. However, recent studies have demonstrated that the internal representations learned in the diffusion models' denoising process are insufficiently meaningful, which can hinder the generation performance. To address this, we propose a novel diffusion U-Net architecture enhanced by explicitly aligning the weaker representation from the earlier layer conditioned on higher noise with the better representation from the later layer conditioned on lower noise during training, which we use for the challenging multimodal task of video background music generation. Our diffusion U-Net Self-Representation Alignment (U-SRA) employs an alignment loss to effectively guide our diffusion model to learn good internal representations, thereby enabling it to produce higher-quality symbolic music conditioned on video features. We employ a cross-attention mechanism to synchronize music with video segments. Experiments on a benchmark video-music dataset demonstrate that the proposed U-SRA approach for video background music generation surpasses the state-of-the-art across key evaluation metrics, including video-music alignment and overall music quality.