The field of video prediction and generation is witnessing a significant shift towards more efficient and practical models, driven by advancements in diffusion models and their adaptations to video processing. Researchers are focusing on reducing computational demands and latency, making these models more viable for real-time applications and deployment on resource-constrained devices such as mobile phones. Key innovations include the treatment of video as a continuous multi-dimensional process, novel distillation techniques for reducing sampling steps, and the development of causal transformers to enable on-the-fly frame generation. These approaches not only enhance efficiency but also maintain or improve the quality of generated videos, as evidenced by state-of-the-art performance on benchmark datasets. Notably, the integration of adversarial distillation schemes and lightweight autoencoders is paving the way for high-quality video editing on mobile devices. Overall, the emphasis is on making video generation and editing more accessible and efficient, with a strong focus on real-time and interactive applications.

Noteworthy papers include one that introduces a continuous video flow model reducing sampling steps by 75%, and another that enables video editing at 12 frames per second on mobile devices through a series of optimizations.