Pose Estimation Benchmark

Pose estimation benchmarks evaluate the accuracy of algorithms that determine the position and orientation of objects (or body parts) in images or videos. Current research focuses on improving robustness to visual ambiguities (like occlusion and viewpoint changes), developing more efficient models (e.g., using graph convolutional networks or leveraging mid-level visual representations), and expanding to new modalities like radar data. These advancements are crucial for applications ranging from robotics and augmented reality to behavioral analysis and healthcare, driving the development of more accurate and versatile pose estimation systems.