  Human modeling and relighting are two fundamental problems in computer vision
and graphics, where high-quality datasets can largely facilitate related
research. However, most existing human datasets only provide multi-view human
images captured under the same illumination. Although valuable for modeling
tasks, they are not readily used in relighting problems. To promote research in
both fields, in this paper, we present UltraStage, a new 3D human dataset that
contains more than 2,000 high-quality human assets captured under both
multi-view and multi-illumination settings. Specifically, for each example, we
provide 32 surrounding views illuminated with one white light and two gradient
illuminations. In addition to regular multi-view images, gradient illuminations
help recover detailed surface normal and spatially-varying material maps,
enabling various relighting applications. Inspired by recent advances in neural
representation, we further interpret each example into a neural human asset
which allows novel view synthesis under arbitrary lighting conditions. We show
our neural human assets can achieve extremely high capture performance and are
capable of representing fine details such as facial wrinkles and cloth folds.
We also validate UltraStage in single image relighting tasks, training neural
networks with virtual relighted data from neural assets and demonstrating
realistic rendering improvements over prior arts. UltraStage will be publicly
available to the community to stimulate significant future developments in
various human modeling and rendering tasks. The dataset is available at
https://miaoing.github.io/RNHA.
