2 Dimensional Human Pose Estimation

2D human pose estimation aims to accurately locate key body joints in images, enabling applications like human-computer interaction and activity recognition. Current research emphasizes developing efficient models, often leveraging transformer architectures or refined convolutional neural networks, to reduce computational costs while maintaining high accuracy, particularly for whole-body pose estimation and challenging scenarios like occlusions and rare viewpoints. Significant efforts also focus on semi-supervised and self-supervised learning techniques to reduce reliance on large, laboriously labeled datasets. These advancements are driving progress in real-time applications across diverse fields, including robotics, healthcare, and sports analysis.