3D Human Pose Estimation

3D human pose estimation aims to accurately determine the three-dimensional positions of human joints from various input modalities, such as images, videos, or point clouds. Current research heavily utilizes transformer-based architectures, graph convolutional networks (GCNs), and diffusion models, often incorporating techniques like temporal modeling, multi-view consistency, and occlusion handling to improve accuracy and robustness. This field is crucial for numerous applications, including human-computer interaction, animation, and healthcare, with ongoing efforts focused on improving generalization to real-world scenarios and handling challenging conditions like occlusions and noisy data. The development of new datasets and benchmarking frameworks is also a significant area of focus, enabling more rigorous evaluation and comparison of different approaches.