Single View Human Reconstruction

Single-view human reconstruction aims to create realistic 3D human models from a single 2D image, a challenging problem due to self-occlusions and missing information. Current research focuses on improving the accuracy and detail of these models using various approaches, including implicit neural representations (like pixel-aligned implicit functions), explicit point-based methods, and diffusion models that hallucinate unseen parts of the body. These advancements leverage large datasets and sophisticated training schemes to achieve state-of-the-art results, significantly impacting fields like computer vision, animation, and virtual reality by enabling more realistic and detailed human representations in digital environments.