  We present a method that enables synthesizing novel views and novel poses of
arbitrary human performers from sparse multi-view images. A key ingredient of
our method is a hybrid appearance blending module that combines the advantages
of the implicit body NeRF representation and image-based rendering. Existing
generalizable human NeRF methods that are conditioned on the body model have
shown robustness against the geometric variation of arbitrary human performers.
Yet they often exhibit blurry results when generalized onto unseen identities.
Meanwhile, image-based rendering shows high-quality results when sufficient
observations are available, whereas it suffers artifacts in sparse-view
settings. We propose Neural Image-based Avatars (NIA) that exploits the best of
those two methods: to maintain robustness under new articulations and
self-occlusions while directly leveraging the available (sparse) source view
colors to preserve appearance details of new subject identities. Our hybrid
design outperforms recent methods on both in-domain identity generalization as
well as challenging cross-dataset generalization settings. Also, in terms of
the pose generalization, our method outperforms even the per-subject optimized
animatable NeRF methods. The video results are available at
https://youngjoongunc.github.io/nia
