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Graph."],"published-print":{"date-parts":[[2023,12,5]]},"abstract":"<jats:p>\n            Reproducing the appearance of arbitrary layered materials has long been a critical challenge in computer graphics, with regard to the demanding requirements of both physical accuracy and low computation cost. Recent studies have demonstrated promising results by learning-based representations that implicitly encode the appearance of complex (layered) materials by neural networks. However, existing generally-learned models often struggle between strong representation ability and high runtime performance, and also lack physical parameters for material editing. To address these concerns, we introduce\n            <jats:italic toggle=\"yes\">MetaLayer<\/jats:italic>\n            , a new methodology leveraging meta-learning for modeling and rendering layered materials. MetaLayer contains two networks: a\n            <jats:italic toggle=\"yes\">BSDFNet<\/jats:italic>\n            that compactly encodes layered materials into implicit neural representations, and a\n            <jats:italic toggle=\"yes\">MetaNet<\/jats:italic>\n            that establishes the mapping between the physical parameters of each material and the weights of its corresponding implicit neural representation. A new positional encoding method and a well-designed training strategy are employed to improve the performance and quality of the neural model. As a new learning-based representation, the proposed MetaLayer model provides both fast responses to material editing and high-quality results for a wide range of layered materials, outperforming existing layered BSDF models.\n          <\/jats:p>","DOI":"10.1145\/3618365","type":"journal-article","created":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T10:20:48Z","timestamp":1701771648000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["MetaLayer: A Meta-Learned BSDF Model for Layered Materials"],"prefix":"10.1145","volume":"42","author":[{"given":"Jie","family":"Guo","sequence":"first","affiliation":[{"name":"State Key Lab for Novel Software Technology, Nanjing University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeru","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Lab for Novel Software Technology, Nanjing University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueyan","family":"He","sequence":"additional","affiliation":[{"name":"State Key Lab for Novel Software Technology, Nanjing University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beibei","family":"Wang","sequence":"additional","affiliation":[{"name":"Nankai University and Nanjing University of Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbin","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Lab for Novel Software Technology, Nanjing University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanwen","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Lab for Novel Software Technology, Nanjing University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling-Qi","family":"Yan","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,12,5]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. 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