  Human avatar has become a novel type of 3D asset with various applications.
Ideally, a human avatar should be fully customizable to accommodate different
settings and environments. In this work, we introduce NECA, an approach capable
of learning versatile human representation from monocular or sparse-view
videos, enabling granular customization across aspects such as pose, shadow,
shape, lighting and texture. The core of our approach is to represent humans in
complementary dual spaces and predict disentangled neural fields of geometry,
albedo, shadow, as well as an external lighting, from which we are able to
derive realistic rendering with high-frequency details via volumetric
rendering. Extensive experiments demonstrate the advantage of our method over
the state-of-the-art methods in photorealistic rendering, as well as various
editing tasks such as novel pose synthesis and relighting. The code is
available at https://github.com/iSEE-Laboratory/NECA.
