The recent advancements in video generation and motion control have significantly enhanced the quality and realism of generated content. A notable trend is the integration of 3D scene modeling with large language models to achieve precise control over scene entities, thereby reducing temporal inconsistencies and physical law violations. This approach not only improves the photorealism of generated scenes but also allows for diverse and customizable outputs. Additionally, there is a growing focus on leveraging pre-trained models and diffusion techniques to animate sketches and control camera motion with fine granularity, addressing the limitations of previous methods in maintaining temporal consistency and shape rigidity. The introduction of training-free approaches for predicting diverse object motions from static images further expands the capabilities of video generation models, enabling more realistic and varied animations. Moreover, the development of sample-efficient, differentiable models for humanlike robot painting styles showcases the potential for robotics to replicate complex human artistic processes. Overall, these innovations are pushing the boundaries of what is possible in video generation and motion control, with a strong emphasis on physical coherence and user-friendly interfaces.