{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:02:23Z","timestamp":1750309343603,"version":"3.41.0"},"reference-count":66,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>High-fidelity 3D assets with materials composed of fibers (including hair), complex layered material shaders, or fine scattering geometry are critical in high-end realistic rendering applications. Rendering such models is computationally expensive due to heavy shaders and long scattering paths. Moreover, implementing the shading and scattering models is non-trivial and has to be done not only in the 3D content authoring software (which is necessarily complex), but also in all downstream rendering solutions. For example, web and mobile viewers for complex 3D assets are desirable, but frequently cannot support the full shading complexity allowed by the authoring application. Our goal is to design a neural representation for 3D assets with complex shading that supports full relightability and full integration into existing renderers. We provide an end-to-end shading solution at the first intersection of a ray with the underlying geometry. All shading and scattering is precomputed and included in the neural asset; no multiple scattering paths need to be traced, and no complex shading models need to be implemented to render our assets, beyond a single neural architecture. We combine an MLP decoder with a feature grid. Shading consists of querying a feature vector, followed by an MLP evaluation producing the final reflectance value. Our method provides high-fidelity shading, close to the ground-truth Monte Carlo estimate even at close-up views. We believe our neural assets could be used in practical renderers, providing significant speed-ups and simplifying renderer implementations.<\/jats:p>","DOI":"10.1145\/3695866","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T03:56:30Z","timestamp":1726199790000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["RNA: Relightable Neural Assets"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1892-7299","authenticated-orcid":false,"given":"Krishna","family":"Mullia","sequence":"first","affiliation":[{"name":"Adobe Research, Adobe Inc, San Francisco, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5926-6266","authenticated-orcid":false,"given":"Fujun","family":"Luan","sequence":"additional","affiliation":[{"name":"Adobe Research, Adobe Inc, San Jose, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8710-2645","authenticated-orcid":false,"given":"Xin","family":"Sun","sequence":"additional","affiliation":[{"name":"Adobe Research, Adobe Inc, San Jose, United States"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3808-6092","authenticated-orcid":false,"given":"Milo\u0161","family":"Ha\u0161an","sequence":"additional","affiliation":[{"name":"Adobe Research, Adobe Inc, San Jose, United States"}]}],"member":"320","published-online":{"date-parts":[[2024,10]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3550469.3555397"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3197517.3201289"},{"key":"e_1_3_3_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1356682.1356686"},{"key":"e_1_3_3_5_1","unstructured":"Sai Bi Zexiang Xu Pratul P. Srinivasan Ben Mildenhall Kalyan Sunkavalli Milos Hasan Yannick Hold-Geoffroy David J. Kriegman and Ravi Ramamoorthi. 2020. Neural reflectance fields for appearance acquisition. arXiv:2008.03824. Retrieved from https:\/\/arxiv.org\/abs\/2008.03824"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01245"},{"key":"e_1_3_3_7_1","first-page":"10691","article-title":"Neural-pil: Neural pre-integrated lighting for reflectance decomposition","author":"Boss Mark","year":"2021","unstructured":"Mark Boss, Varun Jampani, Raphael Braun, Ce Liu, Jonathan Barron, and Hendrik Lensch. 2021b. Neural-pil: Neural pre-integrated lighting for reflectance decomposition. In Proceedings of the 35th International Conference on Neural Information Processing Systems. 10691\u201310704.","journal-title":"Proceedings of the 35th International Conference on Neural Information Processing Systems"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14592"},{"key":"e_1_3_3_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01565"},{"key":"e_1_3_3_10_1","doi-asserted-by":"crossref","unstructured":"Anpei Chen Zexiang Xu Andreas Geiger Jingyi Yu and Hao Su. 2022. TensoRF: Tensorial radiance fields. In European Conference on Computer Vision (ECCV).","DOI":"10.1007\/978-3-031-19824-3_20"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12830"},{"key":"e_1_3_3_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2775280.2792555"},{"key":"e_1_3_3_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3092818"},{"key":"e_1_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/300776.300778"},{"key":"e_1_3_3_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8659.2011.01976.x"},{"key":"e_1_3_3_16_1","first-page":"409","article-title":"A spectral BSSRDF for shading human skin.","volume":"2006","author":"Donner Craig","year":"2006","unstructured":"Craig Donner and Henrik Wann Jensen. 2006. A spectral BSSRDF for shading human skin. Rendering Techniques 2006 (2006), 409\u2013418.","journal-title":"Rendering Techniques"},{"key":"e_1_3_3_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409060.1409093"},{"key":"e_1_3_3_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417767"},{"key":"e_1_3_3_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2016.2617872"},{"key":"e_1_3_3_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275053"},{"key":"e_1_3_3_21_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12148"},{"key":"e_1_3_3_22_1","first-page":"22856","article-title":"Shape, light, and material decomposition from images using Monte Carlo rendering and denoising","author":"Hasselgren Jon","year":"2022","unstructured":"Jon Hasselgren, Nikolai Hofmann, and Jacob Munkberg. 2022. Shape, light, and material decomposition from images using Monte Carlo rendering and denoising. In Proceedings of the 36th International Conference on Neural Information Processing Systems. 22856\u201322869.","journal-title":"Proceedings of the 36th International Conference on Neural Information Processing Systems"},{"key":"e_1_3_3_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/383259.383319"},{"key":"e_1_3_3_24_1","doi-asserted-by":"crossref","unstructured":"Haian Jin Isabella Liu Peijia Xu Xiaoshuai Zhang Songfang Han Sai Bi Xiaowei Zhou Zexiang Xu and Hao Su. 2023. TensoIR: Tensorial inverse rendering. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).","DOI":"10.1109\/CVPR52729.2023.00024"},{"key":"e_1_3_3_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3130800.3130880"},{"key":"e_1_3_3_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2998578"},{"key":"e_1_3_3_27_1","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980. Retrieved from https:\/\/arxiv.org\/abs\/1412.6980"},{"key":"e_1_3_3_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3528223.3530177"},{"issue":"4","key":"e_1_3_3_29_1","first-page":"175","article-title":"NeuMIP: Multi-resolution neural materials","volume":"40","author":"Kuznetsov Alexandr","year":"2021","unstructured":"Alexandr Kuznetsov, Krishna Mullia, Zexiang Xu, Milo\u0161 Ha\u0161an, and Ravi Ramamoorthi. 2021. NeuMIP: Multi-resolution neural materials. Transactions on Graphics (Proceedings of SIGGRAPH) 40, 4, Article 175 (July2021), 13 pages.","journal-title":"Transactions on Graphics (Proceedings of SIGGRAPH)"},{"key":"e_1_3_3_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3528233.3530721"},{"key":"e_1_3_3_31_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14344"},{"key":"e_1_3_3_32_1","unstructured":"Shi Mao Chenming Wu Zhelun Shen and Liangjun Zhang. 2023. NeuS-PIR: Learning relightable neural surface using pre-integrated rendering. arXiv:2306.07632. Retrieved from https:\/\/arxiv.org\/abs\/2306.07632"},{"key":"e_1_3_3_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/882262.882345"},{"key":"e_1_3_3_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"e_1_3_3_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503250"},{"key":"e_1_3_3_36_1","doi-asserted-by":"publisher","unstructured":"Thomas M\u00fcller Alex Evans Christoph Schied and Alexander Keller. 2022. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. 41 4 (July 2022) 102:1\u2013102:15. DOI:10.1145\/3528223.3530127","DOI":"10.1145\/3528223.3530127"},{"key":"e_1_3_3_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00810"},{"key":"e_1_3_3_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/882262.882280"},{"key":"e_1_3_3_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015706.1015749"},{"issue":"2","key":"e_1_3_3_40_1","article-title":"Neural precomputed radiance transfer","volume":"41","author":"Rainer Gilles","year":"2022","unstructured":"Gilles Rainer, Adrien Bousseau, Tobias Ritschel, and George Drettakis. 2022. Neural precomputed radiance transfer. Computer Graphics Forum (Proceedings of the Eurographics conference) 41, 2 (April2022), 365\u2013378. Retrieved from http:\/\/www-sop.inria.fr\/reves\/Basilic\/2022\/RBRD22","journal-title":"Computer Graphics Forum (Proceedings of the Eurographics conference)"},{"key":"e_1_3_3_41_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13633"},{"key":"e_1_3_3_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/1610467"},{"key":"e_1_3_3_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766899"},{"key":"e_1_3_3_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2461912.2462009"},{"key":"e_1_3_3_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/566654.566612"},{"key":"e_1_3_3_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00741"},{"key":"e_1_3_3_47_1","doi-asserted-by":"crossref","unstructured":"Cheng Sun Guangyan Cai Zhengqin Li Kai Yan Cheng Zhang Carl Marshall Jia-Bin Huang Shuang Zhao and Zhao Dong. 2023a. Neural-PBIR Reconstruction of Shape Material and Illumination. arxiv:2304.13445 [cs.CV]. Retrieved from https:\/\/arxiv.org\/abs\/2304.13445","DOI":"10.1109\/ICCV51070.2023.01654"},{"key":"e_1_3_3_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01654"},{"key":"e_1_3_3_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/1141911.1141981"},{"key":"e_1_3_3_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3322974"},{"key":"e_1_3_3_51_1","unstructured":"Magnus Wrenninge Ryusuke Villemin and Christophe Hery. 2017. Path traced subsurface scattering using anisotropic phase functions and non-exponential free flights. Technical report. Pixar Inc."},{"key":"e_1_3_3_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00418"},{"key":"e_1_3_3_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588432.3591524"},{"issue":"6","key":"e_1_3_3_54_1","first-page":"1","article-title":"Anisotropic spherical Gaussians","volume":"32","author":"Xu Kun","year":"2013","unstructured":"Kun Xu, Wei-Lun Sun, Zhao Dong, Dan-Yong Zhao, Run-Dong Wu, and Shi-Min Hu. 2013. Anisotropic spherical Gaussians. ACM Transactions on Graphics (TOG) 32, 6 (2013), 1\u201311.","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"e_1_3_3_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3550469.3555386"},{"key":"e_1_3_3_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818080"},{"key":"e_1_3_3_57_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19821-2_40"},{"key":"e_1_3_3_58_1","doi-asserted-by":"crossref","unstructured":"Sara Fridovich-Keil Alex Yu Matthew Tancik Qinhong Chen Benjamin Recht and Angjoo Kanazawa. 2022. Plenoxels: Radiance Fields without Neural Networks. In CVPR.","DOI":"10.1109\/CVPR52688.2022.00542"},{"key":"e_1_3_3_59_1","doi-asserted-by":"publisher","unstructured":"Tizian Zeltner Fabrice Rousselle Andrea Weidlich Petrik Clarberg Jan Nov\u00e1k Benedikt Bitterli Alex Evans Tom\u00e1\u0161 Davidovi\u010d Simon Kallweit and Aaron Lefohn. 2024. Real-time neural appearance models. ACM Trans. Graph. 43 3 (June 2024). DOI:10.1145\/3659577","DOI":"10.1145\/3659577"},{"key":"e_1_3_3_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588432.3591482"},{"key":"e_1_3_3_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00548"},{"key":"e_1_3_3_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00541"},{"key":"e_1_3_3_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3478513.3480500"},{"key":"e_1_3_3_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01809"},{"key":"e_1_3_3_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02095"},{"issue":"4","key":"e_1_3_3_66_1","first-page":"1","article-title":"Neural complex luminaires: Representation and rendering","volume":"40","author":"Zhu Junqiu","year":"2021","unstructured":"Junqiu Zhu, Yaoyi Bai, Zilin Xu, Steve Bako, Edgar Vel\u00e1zquez-Armend\u00e1riz, Lu Wang, Pradeep Sen, Milo\u0161 Ha\u0161an, and Ling-Qi Yan. 2021. Neural complex luminaires: Representation and rendering. ACM Transactions on Graphics (TOG) 40, 4 (2021), 1\u201312.","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"e_1_3_3_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/1399504.1360631"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3695866","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3695866","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:29Z","timestamp":1750291469000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3695866"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":66,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2,28]]}},"alternative-id":["10.1145\/3695866"],"URL":"https:\/\/doi.org\/10.1145\/3695866","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"type":"print","value":"0730-0301"},{"type":"electronic","value":"1557-7368"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"2024-01-11","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-02","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}