  We present a neural rendering framework for simultaneous view synthesis and
appearance editing of a scene from multi-view images captured under known
environment illumination. Existing approaches either achieve view synthesis
alone or view synthesis along with relighting, without direct control over the
scene's appearance. Our approach explicitly disentangles the appearance and
learns a lighting representation that is independent of it. Specifically, we
independently estimate the BRDF and use it to learn a lighting-only
representation of the scene. Such disentanglement allows our approach to
generalize to arbitrary changes in appearance while performing view synthesis.
We show results of editing the appearance of a real scene, demonstrating that
our approach produces plausible appearance editing. The performance of our view
synthesis approach is demonstrated to be at par with state-of-the-art
approaches on both real and synthetic data.
