Pose Estimation Model

Pose estimation models aim to accurately locate anatomical keypoints in images or videos of humans and animals, enabling applications like human-computer interaction and autonomous driving. Current research emphasizes developing lightweight and efficient models, often employing transformer networks, graph convolutional networks, or adaptations of existing architectures like HRNet, while also focusing on improving robustness to real-world image corruptions and addressing data biases (e.g., underrepresentation of wheelchair users). This field is crucial for advancing various applications, as accurate and robust pose estimation is essential for safe and effective human-machine interaction and other AI-driven systems.