  We present neural radiance fields for rendering and temporal (4D)
reconstruction of humans in motion (H-NeRF), as captured by a sparse set of
cameras or even from a monocular video. Our approach combines ideas from neural
scene representation, novel-view synthesis, and implicit statistical geometric
human representations, coupled using novel loss functions. Instead of learning
a radiance field with a uniform occupancy prior, we constrain it by a
structured implicit human body model, represented using signed distance
functions. This allows us to robustly fuse information from sparse views and
generalize well beyond the poses or views observed in training. Moreover, we
apply geometric constraints to co-learn the structure of the observed subject
-- including both body and clothing -- and to regularize the radiance field to
geometrically plausible solutions. Extensive experiments on multiple datasets
demonstrate the robustness and the accuracy of our approach, its generalization
capabilities significantly outside a small training set of poses and views, and
statistical extrapolation beyond the observed shape.
