{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:30:44Z","timestamp":1780392644416,"version":"3.54.1"},"reference-count":64,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF","award":["IIS-2007283"],"award-info":[{"award-number":["IIS-2007283"]}]},{"name":"EPSRC Early Career Fellowship","award":["EP\/N006259\/1"],"award-info":[{"award-number":["EP\/N006259\/1"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2022,4,30]]},"abstract":"<jats:p>\n            Procedural modeling is now the de facto standard of material modeling in industry. Procedural models can be edited and are easily extended, unlike pixel-based representations of captured materials. In this article, we present a semi-automatic pipeline for general material proceduralization. Given Spatially Varying Bidirectional Reflectance Distribution Functions (SVBRDFs) represented as sets of pixel maps, our pipeline decomposes them into a tree of\n            <jats:italic>sub-materials<\/jats:italic>\n            whose spatial distributions are encoded by their associated mask maps. This semi-automatic decomposition of material maps progresses hierarchically, driven by our new spectrum-aware material matting and instance-based decomposition methods. Each decomposed sub-material is proceduralized by a novel multi-layer noise model to capture local variations at different scales. Spatial distributions of these sub-materials are modeled either by a by-example inverse synthesis method recovering Point Process Texture Basis Functions (PPTBF) [\n            <jats:xref ref-type=\"bibr\">30<\/jats:xref>\n            ] or via random sampling. To reconstruct procedural material maps, we propose a differentiable rendering-based optimization that recomposes all generated procedures together to maximize the similarity between our procedural models and the input material pixel maps. We evaluate our pipeline on a variety of synthetic and real materials. We demonstrate our method\u2019s capacity to process a wide range of material types, eliminating the need for artist designed material graphs required in previous work\u00a0[\n            <jats:xref ref-type=\"bibr\">38<\/jats:xref>\n            ,\n            <jats:xref ref-type=\"bibr\">53<\/jats:xref>\n            ]. As fully procedural models, our results expand to arbitrary resolution and enable high-level user control of appearance.\n          <\/jats:p>","DOI":"10.1145\/3502431","type":"journal-article","created":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T10:32:48Z","timestamp":1641292368000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["An Inverse Procedural Modeling Pipeline for SVBRDF Maps"],"prefix":"10.1145","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3674-295X","authenticated-orcid":false,"given":"Yiwei","family":"Hu","sequence":"first","affiliation":[{"name":"Yale University, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2052-4835","authenticated-orcid":false,"given":"Chengan","family":"He","sequence":"additional","affiliation":[{"name":"Yale University, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6219-3747","authenticated-orcid":false,"given":"Valentin","family":"Deschaintre","sequence":"additional","affiliation":[{"name":"Adobe Research and Imperial College London, London, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2495-4979","authenticated-orcid":false,"given":"Julie","family":"Dorsey","sequence":"additional","affiliation":[{"name":"Yale University, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5241-0886","authenticated-orcid":false,"given":"Holly","family":"Rushmeier","sequence":"additional","affiliation":[{"name":"Yale University, New Haven, CT, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Adobe. 2021. Substance designer. Retrieved from https:\/\/www.substance3d.com\/."},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2018.04.001"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201275"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.32"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/1399504.1360639"},{"key":"e_1_3_1_7_2","article-title":"GPyOpt: A Bayesian optimization framework in Python","author":"authors The GPyOpt","year":"2016","unstructured":"The GPyOpt authors. 2016. GPyOpt: A Bayesian optimization framework in Python. Retrieved from http:\/\/github.com\/SheffieldML\/GPyOpt.","journal-title":"Retrieved from http:\/\/github.com\/SheffieldML\/GPyOpt"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/1576246.1531330"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298970"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.5555\/3305381.3305430"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00404"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.18"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_39"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0872-3"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201378"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13765"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14056"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01531"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.visinf.2019.07.003"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/383259.383296"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.5555\/850924.851569"},{"key":"e_1_3_1_23_2","volume-title":"Doctoral Dissertation, Cornell University","author":"Foo Sing Choong","year":"1997","unstructured":"Sing Choong Foo. 1997. A Gonioreflectometer for Measuring the Bidirectional Reflectance of Material for Use in Illumination Computation. Doctoral Dissertation, Cornell University."},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2010.2052822"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/2185520.2185569"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13073"},{"key":"e_1_3_1_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3323042"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.265"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/2661229.2661249"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.5555\/3059330.3059335"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14061"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13229"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14142"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417779"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/218380.218446"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3233304"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3478513.3480507"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00838"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356516"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12565"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.7763\/IJFCC.2014.V3.274"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/1179352.1141949"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/2070781.2024178"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2008.168"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073641"},{"key":"e_1_3_1_46_2","first-page":"72","article-title":"Materials for masses: SVBRDF acquisition with a single mobile phone image","author":"Li Zhengqin","year":"2018","unstructured":"Zhengqin Li, Kalyan Sunkavalli, and Manmohan Chandraker. 2018. Materials for masses: SVBRDF acquisition with a single mobile phone image. In Proceedings of the European Conference on Computer Vision (ECCV). 72\u201387.","journal-title":"I"},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275055"},{"key":"e_1_3_1_48_2","doi-asserted-by":"crossref","unstructured":"Shervin Minaee Yuri Boykov Fatih Porikli Antonio Plaza Nasser Kehtarnavaz and Demetri Terzopoulos. 2020. Image segmentation using deep learning: A survey. arxiv:2001.05566 [cs.CV]","DOI":"10.1109\/TPAMI.2021.3059968"},{"key":"e_1_3_1_49_2","doi-asserted-by":"publisher","DOI":"10.23915\/distill.00027.003"},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1145\/1276377.1276444"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1145\/882262.882269"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.4310\/AMSA.2018.v3.n1.a4"},{"key":"e_1_3_1_53_2","doi-asserted-by":"publisher","DOI":"10.1145\/1661412.1618453"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417781"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00459"},{"key":"e_1_3_1_56_2","volume-title":"Proceedings of the 3rd International Conference on Learning Representations","author":"Simonyan Karen","year":"2015","unstructured":"Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. In Proceedings of the 3rd International Conference on Learning Representations. Retrieved from http:\/\/arxiv.org\/abs\/1409.1556."},{"key":"e_1_3_1_57_2","first-page":"665","volume-title":"Computer Graphics Forum","author":"\u0160t\u2019ava Ondrej","year":"2010","unstructured":"Ondrej \u0160t\u2019ava, Bedrich Bene\u0161, Radomir M\u011bch, Daniel G. Aliaga, and Peter Kri\u0161tof. 2010. Inverse procedural modeling by automatic generation of l-systems. In Computer Graphics Forum, Vol. 29. Wiley Online Library, 665\u2013674."},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1080\/10867651.2004.10487596"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045533"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0686-2"},{"key":"e_1_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAU.1967.1161901"},{"key":"e_1_3_1_62_2","volume-title":"Eurographics 2009 - State of the Art Reports","author":"Wie Li-Yi","year":"2009","unstructured":"Li-Yi Wie, Sylvain Lefebvre, Vivek Kwatra, and Greg Turk. 2009. State of the art in example-based texture synthesis. In Eurographics 2009 - State of the Art Reports, M. Pauly and G. Greiner (Eds.). The Eurographics Association. DOI:DOI:https:\/\/doi.org\/10.2312\/egst.20091063"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.41"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201285"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3502431","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3502431","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3502431","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:00Z","timestamp":1750191120000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3502431"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,4]]},"references-count":64,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,4,30]]}},"alternative-id":["10.1145\/3502431"],"URL":"https:\/\/doi.org\/10.1145\/3502431","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,4]]},"assertion":[{"value":"2021-09-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-11-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-01-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}