Sequential Generation

Sequential generation focuses on creating ordered sequences of data, aiming to model complex dependencies and generate realistic outputs. Current research emphasizes the use of diffusion models, transformers, and state-space models, often incorporating physical constraints or temporal information to improve generation quality and controllability. These advancements are impacting diverse fields, from robotics and 3D modeling (through physically plausible part generation) to solving complex optimization problems and improving image segmentation accuracy. The development of more robust and efficient sequential generation methods holds significant potential for advancing various scientific and engineering applications.