Object Motion

Object motion research focuses on understanding, predicting, and controlling the movement of objects, particularly in complex scenes and interactions with humans or robots. Current research emphasizes developing robust methods for 3D motion estimation and segmentation from various sensor modalities (e.g., cameras, lidar, IMUs), often employing deep learning architectures like diffusion models, transformer networks, and graph neural networks, alongside classical approaches like Kalman filtering and inverse kinematics. These advancements are crucial for improving applications such as autonomous driving, robotics (especially manipulation and human-robot interaction), and human motion analysis in fields like healthcare and sports. The development of new benchmarks and datasets further facilitates the rigorous evaluation and comparison of different approaches.