Human Mesh Reconstruction

Human mesh reconstruction aims to create accurate 3D models of the human body from images or videos, primarily focusing on overcoming challenges like partial visibility, occlusions, and perspective distortions. Current research emphasizes developing robust methods using various architectures, including transformers, graph neural networks, and parametric models like SMPL, often incorporating techniques like multi-view fusion, test-time adaptation, and self-supervised learning to improve accuracy and efficiency. This field is significant for applications in virtual try-on, augmented reality, human-computer interaction, and motion capture, driving advancements in both computer vision and 3D modeling.