Photorealistic Material Editing Through Direct Image Manipulation
KAUST DepartmentComputer Science Program
Visual Computing Center (VCC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Preprint Posting Date2019-09-12
Online Publication Date2020-07-20
Print Publication Date2020-07
Embargo End Date2021-07-20
Permanent link to this recordhttp://hdl.handle.net/10754/660691
MetadataShow full item record
AbstractCreating 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.
CitationZsolnai-Fehér, K., Wonka, P., & Wimmer, M. (2020). Photorealistic Material Editing Through Direct Image Manipulation. Computer Graphics Forum, 39(4), 107–120. doi:10.1111/cgf.14057
SponsorsWe would like to thank Reynante Martinez for providing us the geometry and some of the materials for the Paradigm (Fig. 1) and Genesis scenes (Fig. 3), ianofshields for the Liquify scene that served as a basis for Fig. 9, Robin Marin for the material test scene, Andrew Price and Gábor Mészáros for their help with geometry modeling, Felícia Zsolnai-Fehér for her help improving our figures, Christian Freude, David Ha, Philipp Erler and Adam Celarek for their useful comments. We also thank the anonymous reviewers for their help improving this manuscript and NVIDIA for providing the hardware to train our neural networks. This work was partially funded by Austrian Science Fund (FWF), project number P27974.
JournalComputer Graphics Forum