3D Body

3D body modeling aims to accurately reconstruct and analyze the three-dimensional structure and movement of the human body from visual data, primarily images and videos. Current research focuses on improving the robustness of these models to challenges like occlusions and partial views, often employing neural networks, particularly graph convolutional networks and hybrid analytical-neural approaches like inverse kinematics, to achieve accurate and realistic 3D body representations. These advancements are crucial for applications ranging from virtual try-on and robotic-assisted therapies to motion capture and human-computer interaction, driving improvements in efficiency and accuracy through techniques such as active learning and data augmentation. The development of comprehensive, high-quality datasets is also a key area of ongoing work.