Explicit Motion

Explicit motion modeling in computer vision and related fields focuses on representing and utilizing motion information directly, rather than relying solely on implicit representations derived from static frames. Current research emphasizes developing novel architectures, such as transformers and diffusion models, to effectively capture and leverage motion features for tasks like video generation, action recognition, and anomaly detection. This explicit approach improves the accuracy and robustness of various applications, particularly in scenarios with complex or significant motion, leading to advancements in areas such as video quality assessment and human-computer interaction. The resulting improvements in motion understanding have broad implications for fields ranging from robotics to healthcare.