  Recent neural human representations can produce high-quality multi-view
rendering but require using dense multi-view inputs and costly training. They
are hence largely limited to static models as training each frame is
infeasible. We present HumanNeRF - a generalizable neural representation - for
high-fidelity free-view synthesis of dynamic humans. Analogous to how IBRNet
assists NeRF by avoiding per-scene training, HumanNeRF employs an aggregated
pixel-alignment feature across multi-view inputs along with a pose embedded
non-rigid deformation field for tackling dynamic motions. The raw HumanNeRF can
already produce reasonable rendering on sparse video inputs of unseen subjects
and camera settings. To further improve the rendering quality, we augment our
solution with an appearance blending module for combining the benefits of both
neural volumetric rendering and neural texture blending. Extensive experiments
on various multi-view dynamic human datasets demonstrate the generalizability
and effectiveness of our approach in synthesizing photo-realistic free-view
humans under challenging motions and with very sparse camera view inputs.
