Unsupervised 3D Pose

Unsupervised 3D pose estimation aims to recover three-dimensional pose information (e.g., human body pose, camera trajectory) from images or videos without relying on labeled training data. Current research focuses on leveraging techniques like contrastive learning, normalizing flows, and incorporating spatial cues (e.g., feature flows, depth information) to improve accuracy and robustness, often employing neural networks and structure-from-motion principles. These advancements are significant because they enable training on large, readily available unlabeled datasets, potentially leading to more generalizable and efficient 3D pose estimation systems for applications such as autonomous driving and human-computer interaction.