Learning non-stationary SVBRDFs using GANs and differentiable rendering

• Main Author: Duinkharjav, Budmonde.
• Other Authors: Frédo Durand.
• Format: Others
• Language: English
• Published: Massachusetts Institute of Technology 2019
• Subjects: Electrical Engineering and Computer Science.
• Online Access: https://hdl.handle.net/1721.1/123018

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. === Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 === Cataloged from student-submitted PDF version of thesis. === Includes bibliographical references (pages 43-45). === In this thesis we propose a learning approach for generating realistic SVBRDFs using generative adversarial models and differentiable rendering. Our model learns a mapping from the geometry buffer of a surface to a corresponding albedo texture-map by training on images of the same surface rendered using a target texture-map. A key feature of this learning process is the ability to differentiate the render function within our model; this enables the optimization of the texture-map generator parameters using a loss function computed from the rendered 2D images. Our results show that differentiable rendering is applicable in complex neural network models such as GANs, opening up opportunities for more applications of deep learning methods in the computer graphics pipeline. === by Budmonde Duinkharjav. === M. Eng. === M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science