Temporal Motion Propagation

Temporal motion propagation focuses on efficiently and accurately transferring information, such as motion fields or visual appearance, across consecutive frames in video or dynamic graph data. Current research emphasizes developing models that leverage the inherent temporal correlations between frames, employing techniques like diffusion-based generation, graph neural networks with temporal propagation, and two-stream architectures combining local and global feature interactions to improve accuracy and computational efficiency. These advancements have significant implications for various applications, including video super-resolution, video editing, and dynamic graph analysis, by enabling more realistic and efficient processing of temporal data.