The field of video generation and understanding is rapidly advancing, with a focus on improving temporal consistency, efficiency, and accuracy. Recent developments have led to the creation of innovative methods for video generation, including the use of diffusion models, stereo matching, and geometry-aware conditions. These approaches have shown significant improvements in generating high-quality, temporally consistent videos, and have the potential to be applied in a variety of applications, such as video editing, surgical simulation, and 3D texture synthesis. Notable papers in this area include FastInit, which introduces a fast noise initialization method for video generation, and StereoDiff, which synergizes stereo matching with video depth diffusion for consistent video depth estimation. Additionally, papers like DFVEdit and HieraSurg have demonstrated impressive results in zero-shot video editing and surgical video generation, respectively. These advancements are paving the way for more realistic and engaging video content, and are expected to have a significant impact on the field of computer vision and graphics.