Pose Label

Pose label research focuses on accurately determining the position and orientation (pose) of objects, particularly humans and objects in images and videos, often using computer vision techniques. Current research emphasizes developing robust methods that handle challenging conditions like occlusions and low light, leveraging synthetic data generation and self-supervised learning to overcome the limitations of scarce and expensive real-world labeled datasets. This work employs various model architectures, including transformers, ControlNets, and neural networks designed for specific tasks like pose regression and mesh reconstruction, aiming to improve accuracy and generalization across different domains. Advances in pose label estimation have significant implications for robotics, augmented reality, and human-computer interaction applications.