Motion Annotation

Motion annotation focuses on accurately labeling and representing movement data, crucial for applications ranging from human motion analysis in healthcare to generating realistic animations. Current research emphasizes developing efficient and accurate annotation methods, often leveraging deep learning models like Transformers and graph neural networks, and exploring the use of synthetic data and weakly supervised learning techniques to address data scarcity issues. These advancements are improving the quality and quantity of annotated motion data, enabling more robust and reliable analysis of human and object movement across diverse fields.