The field of generative modeling and video generation is rapidly advancing, with a focus on improving efficiency, quality, and interactivity. Recent developments have led to the creation of novel frameworks and models that enable high-quality video synthesis, real-time interaction, and enhanced motion control. Notably, the integration of score-based diffusion models, autoregressive modeling, and distribution matching distillation has resulted in significant improvements in generation quality and efficiency. Furthermore, the introduction of new architectures and techniques, such as phased distillation, cross-fluctuation phase transitions, and scalable autoregressive modeling, has expanded the capabilities of generative models. These advancements have far-reaching implications for various applications, including text-to-video generation, motion editing, and video streaming.

Some noteworthy papers in this area include: Phased DMD, which proposes a multi-step distillation framework that enhances model capacity and preserves output diversity. MotionStream, which enables real-time video generation with interactive motion controls, achieving sub-second latency and high-quality video synthesis. InfinityStar, which introduces a unified spacetime autoregressive framework for high-resolution image and dynamic video synthesis, outperforming existing autoregressive models and diffusion-based methods.