Modal Diffusion

Modal diffusion models are generative AI frameworks that leverage diffusion processes to create data across multiple modalities (e.g., images, text, audio). Current research focuses on improving the quality and coherence of multi-modal generation using architectures like multi-modal U-Nets and transformers, often incorporating contrastive learning or other techniques to enhance alignment between modalities. This approach is proving valuable in diverse applications, including medical image analysis, 3D model generation, and co-speech gesture synthesis, by enabling more realistic and nuanced data generation than single-modality methods. The ability to seamlessly integrate and generate information across different data types holds significant potential for advancing various scientific fields and practical applications.