3D Human Shape

3D human shape reconstruction aims to create accurate, detailed three-dimensional models of the human body from various input sources like single images, multi-view images, or videos. Current research heavily utilizes deep learning, particularly neural implicit representations and transformer architectures, often incorporating parametric models like SMPL to improve accuracy and efficiency. This field is crucial for advancements in virtual reality, animation, healthcare (e.g., personalized medicine), and fashion (e.g., virtual try-ons), driving the development of novel algorithms and datasets to address challenges like occlusion, pose variation, and clothing effects.