The field of video analysis is moving towards leveraging motion and depth information to improve performance in various tasks such as salient object detection, video object segmentation, and video distillation. Researchers are exploring innovative methods to transfer motion knowledge from pre-trained video diffusion models to generate realistic training data, and to exploit structural correlations between depth and flow for unsupervised video object segmentation. Additionally, there is a growing interest in developing selective fusion strategies to effectively utilize optical flow and depth in RGB-D video salient object detection. These advancements have led to significant improvements in performance across multiple benchmarks, demonstrating the potential of motion and depth-based approaches in video analysis. Noteworthy papers include: TransFlow, which achieves improved performance in video salient object detection by transferring motion knowledge from pre-trained video diffusion models. DepthFlow, which proposes a novel data generation method that synthesizes optical flow from single images, achieving new state-of-the-art performance on public video object segmentation benchmarks. Unleashing the Power of Motion and Depth, which proposes a selective fusion strategy for RGB-D video salient object detection, demonstrating superiority over other models on comprehensive benchmarks. GVD, which proposes a diffusion-based video distillation method, achieving state-of-the-art performance on benchmark video datasets while significantly reducing computational cost.