Visual Generative Models: Past, Present, and Future

Recent breakthroughs in generative adversarial networks, diffusion and autoregressive models have dramatically advanced the state of visual content generation, including widespread applications in generating images, videos, 3D objects, and more. These advancements not only push the frontiers of synthesis quality and scalability but also unlock new applications in design, entertainment, vision, scientific domains, and even improving or reformulating the vision tasks. However, several fundamental and practical challenges remain, e.g., improving controllability, enhancing fidelity and realism, scaling across modalities, ensuring alignment with human values, and achieving efficient, safe deployment. This workshop aims to provide a broad forum for exploring the past breakthroughs, current developments, and future directions of visual generative models, with particular emphasis on foundational innovations, emerging challenges, and practical applications.