The recent advancements in video processing and generation have significantly pushed the boundaries of what is possible in the field. A notable trend is the integration of diffusion models with attention mechanisms to enhance motion transfer and video decomposition. These models are being repurposed to handle complex tasks such as amodal segmentation, video relighting, and motion intensity modulation, demonstrating a shift towards more sophisticated and versatile video processing techniques. The use of diffusion models in video summarization and restoration also highlights their potential in addressing long-standing challenges in video quality and content retention. Notably, the introduction of novel frameworks like MotionFlow and MotionShop, which leverage attention-driven motion transfer and mixture of score guidance respectively, are setting new benchmarks in the field. These innovations not only improve the fidelity and versatility of video generation but also pave the way for more creative and controlled video editing experiences. Additionally, the development of motion estimators and the decoupling of motion intensity modulation in image-to-video generation are significant steps towards more accurate and scalable video processing solutions. Overall, the field is witnessing a rapid evolution towards more nuanced and controllable video generation and processing, driven by advancements in diffusion models and attention-based techniques.