Body Model
Body models in computer vision and robotics aim to create accurate, detailed digital representations of the human body, encompassing shape, pose, and appearance, often including clothing. Current research focuses on improving the robustness and realism of these models, particularly in handling occlusions and diverse body types, employing techniques like parametric models (e.g., SMPL, SMPL-X), implicit neural representations (e.g., NeRFs, SDFs), and transformer networks. These advancements have significant implications for applications such as virtual reality, animation, human-computer interaction, and even public health research by enabling more realistic simulations and analyses of human movement and behavior.
Papers
Probabilistic Estimation of 3D Human Shape and Pose with a Semantic Local Parametric Model
Akash Sengupta, Ignas Budvytis, Roberto Cipolla
LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies
Sandro Lombardi, Bangbang Yang, Tianxing Fan, Hujun Bao, Guofeng Zhang, Marc Pollefeys, Zhaopeng Cui