{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T11:41:17Z","timestamp":1762429277091,"version":"3.41.0"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2016,11,11]],"date-time":"2016-11-11T00:00:00Z","timestamp":1478822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"ERC Starting Grant SmartGeometry","award":["StG-2013- 335373"],"award-info":[{"award-number":["StG-2013- 335373"]}]},{"name":"Microsoft PhD scholarship"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2016,11,11]]},"abstract":"<jats:p>Large 3D model repositories of common objects are now ubiquitous and are increasingly being used in computer graphics and computer vision for both analysis and synthesis tasks. However, images of objects in the real world have a richness of appearance that these repositories do not capture, largely because most existing 3D models are untextured. In this work we develop an automated pipeline capable of transporting texture information from images of real objects to 3D models of similar objects. This is a challenging problem, as an object's texture as seen in a photograph is distorted by many factors, including pose, geometry, and illumination. These geometric and photometric distortions must be undone in order to transfer the pure underlying texture to a new object --- the 3D model. Instead of using problematic dense correspondences, we factorize the problem into the reconstruction of a set of base textures (materials) and an illumination model for the object in the image. By exploiting the geometry of the similar 3D model, we reconstruct certain reliable texture regions and correct for the illumination, from which a full texture map can be recovered and applied to the model. Our method allows for large-scale unsupervised production of richly textured 3D models directly from image data, providing high quality virtual objects for 3D scene design or photo editing applications, as well as a wealth of data for training machine learning algorithms for various inference tasks in graphics and vision.<\/jats:p>","DOI":"10.1145\/2980179.2982404","type":"journal-article","created":{"date-parts":[[2016,11,11]],"date-time":"2016-11-11T17:02:54Z","timestamp":1478883774000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["Unsupervised texture transfer from images to model collections"],"prefix":"10.1145","volume":"35","author":[{"given":"Tuanfeng Y.","family":"Wang","sequence":"first","affiliation":[{"name":"University College London"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Su","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qixing","family":"Huang","sequence":"additional","affiliation":[{"name":"TTIC\/UT Austin"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwei","family":"Huang","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leonidas","family":"Guibas","sequence":"additional","affiliation":[{"name":"Stanford University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Niloy J.","family":"Mitra","sequence":"additional","affiliation":[{"name":"University College London"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2016,12,5]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.487"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12723"},{"key":"e_1_2_2_3_1","doi-asserted-by":"crossref","unstructured":"Barron J. T. and Malik J. 2015. Shape illumination and reflectance from shading. TPAMI.  Barron J. T. and Malik J. 2015. Shape illumination and reflectance from shading. TPAMI.","DOI":"10.1109\/TPAMI.2014.2377712"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601206"},{"key":"e_1_2_2_5_1","volume-title":"Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012.","author":"Chang A. X.","year":"2015","unstructured":"Chang , A. X. , Funkhouser , T. , Guibas , L. , Hanrahan , P. , Huang , Q. , Li , Z. , Savarese , S. , Savva , M. , Song , S. , Su , H. , 2015 . Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012. Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al. 2015. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012."},{"key":"e_1_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Choy C. B. Xu D. Gwak J. Chen K. and Savarese S. 2016. 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. arXiv preprint arXiv:1604.00449.  Choy C. B. Xu D. Gwak J. Chen K. and Savarese S. 2016. 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. arXiv preprint arXiv:1604.00449.","DOI":"10.1007\/978-3-319-46484-8_38"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2185520.2185578"},{"key":"e_1_2_2_8_1","volume-title":"Imagenet: A large-scale hierarchical image database. In CVPR.","author":"Deng J.","year":"2009","unstructured":"Deng , J. , Dong , W. , Socher , R. , Li , L.-J. , Li , K. , and Fei-Fei , L. 2009 . Imagenet: A large-scale hierarchical image database. In CVPR. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. 2009. Imagenet: A large-scale hierarchical image database. In CVPR."},{"volume-title":"Proceedings of the IEEE CVPR, 1538--1546","author":"Dosovitskiy A.","key":"e_1_2_2_9_1","unstructured":"Dosovitskiy , A. , Tobias Springenberg , J. , and Brox , T . 2015. Learning to generate chairs with convolutional neural networks . In Proceedings of the IEEE CVPR, 1538--1546 . Dosovitskiy, A., Tobias Springenberg, J., and Brox, T. 2015. Learning to generate chairs with convolutional neural networks. In Proceedings of the IEEE CVPR, 1538--1546."},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/383259.383296"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601185"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0713-9"},{"key":"e_1_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925870"},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766890"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818097"},{"key":"e_1_2_2_16_1","doi-asserted-by":"crossref","DOI":"10.1214\/aos\/1079120141","article-title":"Mm algorithms for generalized bradley-terry models","author":"Hunter D. R.","year":"2004","unstructured":"Hunter , D. R. 2004 . Mm algorithms for generalized bradley-terry models . Annals of Statistics, 384--406. Hunter, D. R. 2004. Mm algorithms for generalized bradley-terry models. Annals of Statistics, 384--406.","journal-title":"Annals of Statistics, 384--406."},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601209"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33786-4_34"},{"key":"e_1_2_2_19_1","doi-asserted-by":"crossref","unstructured":"Lim J. J. Khosla A. and Torralba A. 2014. FPM: fine pose parts-based model with 3d CAD models. In ECCV 478--493.  Lim J. J. Khosla A. and Torralba A. 2014. FPM: fine pose parts-based model with 3d CAD models. In ECCV 478--493.","DOI":"10.1007\/978-3-319-10599-4_31"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.147"},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766898"},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1023\/B:VISI.0000029664.99615.94"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.178"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601159"},{"key":"e_1_2_2_25_1","doi-asserted-by":"crossref","unstructured":"Su H. Wang F. Yi L. and Guibas L. 2014. 3d-assisted image feature synthesis for novel views of an object. arXiv preprint arXiv:1412.0003.  Su H. Wang F. Yi L. and Guibas L. 2014. 3d-assisted image feature synthesis for novel views of an object. arXiv preprint arXiv:1412.0003.","DOI":"10.1109\/ICCV.2015.307"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.308"},{"key":"e_1_2_2_27_1","unstructured":"Tatarchenko M. Dosovitskiy A. and Brox T. 2015. Single-view to multi-view: Reconstructing unseen views with a convolutional network. CoRR abs\/1511.06702.  Tatarchenko M. Dosovitskiy A. and Brox T. 2015. Single-view to multi-view: Reconstructing unseen views with a convolutional network. CoRR abs\/1511.06702."},{"key":"e_1_2_2_28_1","unstructured":"Vallet B. and Lvy B. 2009. What you seam is what you get. Tech. rep. INRIA - ALICE Project Team.  Vallet B. and Lvy B. 2009. What you seam is what you get. Tech. rep. INRIA - ALICE Project Team."},{"key":"e_1_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2508363.2508393"},{"key":"e_1_2_2_30_1","unstructured":"Wei L.-Y. Lefebvre S. Kwatra V. and Turk G. 2009. State of the art in example-based texture synthesis. In EG-STAR.  Wei L.-Y. Lefebvre S. Kwatra V. and Turk G. 2009. State of the art in example-based texture synthesis. In EG-STAR."},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-012-0515-x"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2185520.2185595"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2980179.2982404","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2980179.2982404","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:49:57Z","timestamp":1750218597000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2980179.2982404"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,11,11]]},"references-count":32,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2016,11,11]]}},"alternative-id":["10.1145\/2980179.2982404"],"URL":"https:\/\/doi.org\/10.1145\/2980179.2982404","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"type":"print","value":"0730-0301"},{"type":"electronic","value":"1557-7368"}],"subject":[],"published":{"date-parts":[[2016,11,11]]},"assertion":[{"value":"2016-12-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}