Multimodal Generation

Multimodal generation focuses on creating coherent outputs across different data types, such as text, images, audio, and video, aiming to build AI systems that understand and generate information in a more human-like way. Current research emphasizes integrating autoregressive models for global context and diffusion models for high-quality local details, often leveraging large language models to manage complex interactions between modalities. This field is significant for advancing AI capabilities in creative content generation, personalized experiences, and complex tasks like robotic control and medical image analysis, driving progress in both fundamental AI research and practical applications.