Human Pose

Human pose estimation, the task of determining the 3D configuration of a human body from images or sensor data, aims to accurately and efficiently capture human movement and posture. Current research focuses on improving robustness to challenges like occlusions, variations in viewpoint and lighting, and data scarcity, often employing diffusion models, transformers, and graph convolutional networks to achieve this. These advancements are driving progress in diverse applications, including human-computer interaction, animation, robotics, healthcare (e.g., gait analysis), and activity recognition, by enabling more accurate and nuanced understanding of human motion. The field is also actively addressing issues of data quality and bias in training datasets to enhance the reliability and generalizability of pose estimation models.