3D Human Pose

3D human pose estimation aims to reconstruct a person's 3D skeletal structure from various input modalities, such as images, depth maps, or point clouds, with a primary objective of achieving accurate and robust pose estimation even under challenging conditions like occlusions or limited viewpoints. Current research heavily utilizes deep learning models, including transformers and diffusion models, often incorporating multi-view fusion techniques and leveraging synthetic data for training and evaluation. This field is crucial for numerous applications, including human-computer interaction, robotics, animation, and healthcare, driving advancements in areas like human-robot collaboration and motion capture technology.