  Creating photorealistic materials for light transport algorithms requires
carefully fine-tuning a set of material properties to achieve a desired
artistic effect. This is typically a lengthy process that involves a trained
artist with specialized knowledge. In this work, we present a technique that
aims to empower novice and intermediate-level users to synthesize high-quality
photorealistic materials by only requiring basic image processing knowledge. In
the proposed workflow, the user starts with an input image and applies a few
intuitive transforms (e.g., colorization, image inpainting) within a 2D image
editor of their choice, and in the next step, our technique produces a
photorealistic result that approximates this target image. Our method combines
the advantages of a neural network-augmented optimizer and an encoder neural
network to produce high-quality output results within 30 seconds. We also
demonstrate that it is resilient against poorly-edited target images and
propose a simple extension to predict image sequences with a strict time budget
of 1-2 seconds per image.
