Human Performance Capture

Human performance capture aims to accurately reconstruct 3D human motion and appearance from various input sources, primarily to create realistic digital representations for applications like VR/AR and film. Current research heavily utilizes deep learning, focusing on neural radiance fields and other neural network architectures to achieve high-fidelity capture from sparse or even monocular video data, often incorporating techniques like pose estimation and surface deformation modeling. This field is crucial for advancing realistic human-computer interaction and immersive experiences, driving improvements in both the accuracy and efficiency of 3D human modeling.