Pose Augmentation

Pose augmentation is a data augmentation technique used to improve the robustness and generalization of models in computer vision tasks involving human poses, such as 3D human pose estimation and person re-identification. Current research focuses on generating diverse and realistic pose variations using methods like diffusion models, generative adversarial networks, and forward kinematics models, often incorporating techniques like contrastive learning and domain adaptation to bridge the gap between training and real-world data. These advancements are crucial for improving the accuracy and reliability of pose estimation in challenging scenarios, ultimately impacting applications in areas like human-computer interaction, robotics, and healthcare.