  Human portraits exhibit various appearances when observed from different
views under different lighting conditions. We can easily imagine how the face
will look like in another setup, but computer algorithms still fail on this
problem given limited observations. To this end, we present a system for
portrait view synthesis and relighting: given multiple portraits, we use a
neural network to predict the light-transport field in 3D space, and from the
predicted Neural Light-transport Field (NeLF) produce a portrait from a new
camera view under a new environmental lighting. Our system is trained on a
large number of synthetic models, and can generalize to different synthetic and
real portraits under various lighting conditions. Our method achieves
simultaneous view synthesis and relighting given multi-view portraits as the
input, and achieves state-of-the-art results.
