3D Human Recovery

3D human recovery aims to reconstruct accurate, textured 3D human models from images or videos, a challenging inverse problem crucial for applications like animation, virtual reality, and healthcare. Current research focuses on improving the accuracy and efficiency of reconstruction using various approaches, including neural networks (e.g., recurrent fitting networks, diffusion models), implicit surface representations, and the incorporation of geometric priors (e.g., leveraging semantic graphs or SMPL models). These advancements are driven by the need for more robust and realistic 3D human models, particularly in handling challenging scenarios like occlusions and variations in pose and appearance, ultimately impacting fields requiring high-fidelity human representation.