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Frédo Durand. Duinkharjav, Budmonde. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2019-11-22T00:02:36Z 2019-11-22T00:02:36Z 2019 2019 https://hdl.handle.net/1721.1/123018 1127619992 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 45 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Learning non-stationary SVBRDFs using GANs and differentiable rendering Thesis application/pdf 2019-11-22T00:02:35Z Publication Master EECS unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="1d7b0e2e-4aa5-43b6-b1b9-bd80b6a66d57"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Learning non-stationary SVBRDFs using GANs and differentiable rendering</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Duinkharjav, Budmonde.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>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.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>