  There has been rapid progress recently on 3D human rendering, including novel
view synthesis and pose animation, based on the advances of neural radiance
fields (NeRF). However, most existing methods focus on person-specific training
and their training typically requires multi-view videos. This paper deals with
a new challenging task -- rendering novel views and novel poses for a person
unseen in training, using only multiview images as input. For this task, we
propose a simple yet effective method to train a generalizable NeRF with
multiview images as conditional input. The key ingredient is a dedicated
representation combining a canonical NeRF and a volume deformation scheme.
Using a canonical space enables our method to learn shared properties of human
and easily generalize to different people. Volume deformation is used to
connect the canonical space with input and target images and query image
features for radiance and density prediction. We leverage the parametric 3D
human model fitted on the input images to derive the deformation, which works
quite well in practice when combined with our canonical NeRF. The experiments
on both real and synthetic data with the novel view synthesis and pose
animation tasks collectively demonstrate the efficacy of our method.
