  Existing Human NeRF methods for reconstructing 3D humans typically rely on
multiple 2D images from multi-view cameras or monocular videos captured from
fixed camera views. However, in real-world scenarios, human images are often
captured from random camera angles, presenting challenges for high-quality 3D
human reconstruction. In this paper, we propose SHERF, the first generalizable
Human NeRF model for recovering animatable 3D humans from a single input image.
SHERF extracts and encodes 3D human representations in canonical space,
enabling rendering and animation from free views and poses. To achieve
high-fidelity novel view and pose synthesis, the encoded 3D human
representations should capture both global appearance and local fine-grained
textures. To this end, we propose a bank of 3D-aware hierarchical features,
including global, point-level, and pixel-aligned features, to facilitate
informative encoding. Global features enhance the information extracted from
the single input image and complement the information missing from the partial
2D observation. Point-level features provide strong clues of 3D human
structure, while pixel-aligned features preserve more fine-grained details. To
effectively integrate the 3D-aware hierarchical feature bank, we design a
feature fusion transformer. Extensive experiments on THuman, RenderPeople,
ZJU_MoCap, and HuMMan datasets demonstrate that SHERF achieves state-of-the-art
performance, with better generalizability for novel view and pose synthesis.
