Pose Correction

Pose correction focuses on refining inaccurate estimations of object or body positions in images or 3D space, aiming for improved accuracy and robustness in various applications. Current research emphasizes developing methods that leverage deep learning, particularly transformer networks and diffusion models, to achieve this correction, often incorporating iterative refinement processes or disentangled feature representations. These advancements are crucial for enhancing the performance of tasks like human-object interaction detection, collaborative autonomous driving, and surgical navigation, where precise pose information is essential for reliable operation.