{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T06:10:35Z","timestamp":1784268635912,"version":"3.55.0"},"reference-count":73,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"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":[[2024,10,31]]},"abstract":"<jats:p>\n            Material reconstruction from a photograph is a key component of 3D content creation democratization. We propose to formulate this ill-posed problem as a controlled synthesis one, leveraging the recent progress in generative deep networks. We present ControlMat, a method which, given a single photograph with uncontrolled illumination as input, conditions a diffusion model to generate plausible, tileable, high-resolution physically-based digital materials. We carefully analyze the behavior of diffusion models for multi-channel outputs, adapt the sampling process to fuse multi-scale information and introduce rolled diffusion to enable both tileability and patched diffusion for high-resolution outputs. Our generative approach further permits exploration of a variety of materials that could correspond to the input image, mitigating the unknown lighting conditions. We show that our approach outperforms recent inference and latent-space optimization methods, and we carefully validate our diffusion process design choices.\n            <jats:xref ref-type=\"fn\">\n              <jats:sup>1<\/jats:sup>\n            <\/jats:xref>\n          <\/jats:p>\n          <jats:p\/>","DOI":"10.1145\/3688830","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T10:16:30Z","timestamp":1724753790000},"page":"1-17","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":39,"title":["ControlMat: A Controlled Generative Approach to Material Capture"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5009-4365","authenticated-orcid":false,"given":"Giuseppe","family":"Vecchio","sequence":"first","affiliation":[{"name":"Research, Adobe Systems Inc, Lyon, France and Electrical Electronic and Computer Engineering, University of Catania, Catania, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6812-2664","authenticated-orcid":false,"given":"Rosalie","family":"Martin","sequence":"additional","affiliation":[{"name":"Research, Adobe Systems Inc, Lyon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6483-240X","authenticated-orcid":false,"given":"Arthur","family":"Roullier","sequence":"additional","affiliation":[{"name":"Research, Adobe Systems Inc, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5998-3932","authenticated-orcid":false,"given":"Adrien","family":"Kaiser","sequence":"additional","affiliation":[{"name":"Research, Adobe Systems Inc, Lyon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4412-7017","authenticated-orcid":false,"given":"Romain","family":"Rouffet","sequence":"additional","affiliation":[{"name":"Research, Adobe Systems Inc, Lyon, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6219-3747","authenticated-orcid":false,"given":"Valentin","family":"Deschaintre","sequence":"additional","affiliation":[{"name":"Research, Adobe Systems Inc, London, United Kingdom of Great Britain and Northern Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5985-0921","authenticated-orcid":false,"given":"Tamy","family":"Boubekeur","sequence":"additional","affiliation":[{"name":"Research, Adobe Systems Inc, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"e_1_3_4_2_1","unstructured":"Adobe. 2022. Substance Source. (2022). Retrieved July 2022 from https:\/\/substance3d.adobe.com\/assets\/"},{"key":"e_1_3_4_3_1","unstructured":"Pranav Aggarwal Hareesh Ravi Naveen Marri Sachin Kelkar Fengbin Chen Vinh Khuc Midhun Harikumar Ritiz Tambi Sudharshan Reddy Kakumanu Purvak Lapsiya Alvin Ghouas Sarah Saber Malavika Ramprasad Baldo Faieta and Ajinkya Kale. 2023. Controlled and conditional text to image generation with diffusion prior. arxiv:cs.CV\/2302.11710. Retrieved from https:\/\/arxiv.org\/abs\/2302.11710"},{"key":"e_1_3_4_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925917"},{"key":"e_1_3_4_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766967"},{"key":"e_1_3_4_6_1","first-page":"214","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Arjovsky Martin","year":"2017","unstructured":"Martin Arjovsky, Soumith Chintala, and L\u00e9on Bottou. 2017. Wasserstein generative adversarial networks. In Proceedings of the International Conference on Machine Learning. PMLR, 214\u2013223."},{"key":"e_1_3_4_7_1","unstructured":"Omer Bar-Tal Lior Yariv Yaron Lipman and Tali Dekel. 2023. MultiDiffusion: fusing diffusion paths for controlled image generation. In Proceedings of the 40th International Conference on Machine Learning (ICML\u201923) JMLR.org Honolulu Hawaii USA."},{"key":"e_1_3_4_8_1","unstructured":"Andrew Brock Jeff Donahue and Karen Simonyan. 2018. Large scale GAN training for high fidelity natural image synthesis. arXiv:1809.11096. Retrieved from https:\/\/arxiv.org\/abs\/1809.11096"},{"key":"e_1_3_4_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/357290.357293"},{"key":"e_1_3_4_10_1","unstructured":"Bin Dai and David Wipf. 2019. Diagnosing and enhancing VAE models. International Conference on Learning Representations."},{"issue":"128","key":"e_1_3_4_11_1","first-page":"15","article-title":"Single-image SVBRDF capture with a rendering-aware deep network","volume":"37","author":"Deschaintre Valentin","year":"2018","unstructured":"Valentin Deschaintre, Miika Aittala, Fr\u00e9do Durand, George Drettakis, and Adrien Bousseau. 2018. Single-image SVBRDF capture with a rendering-aware deep network. ACM Transactions on Graphics (SIGGRAPH Conference Proceedings) 37, 128 (Aug2018), 15. Retrieved from http:\/\/www-sop.inria.fr\/reves\/Basilic\/2018\/DADDB18","journal-title":"ACM Transactions on Graphics (SIGGRAPH Conference Proceedings)"},{"issue":"4","key":"e_1_3_4_12_1","first-page":"13","article-title":"Flexible SVBRDF capture with a multi-image deep network","volume":"38","author":"Deschaintre Valentin","year":"2019","unstructured":"Valentin Deschaintre, Miika Aittala, Fr\u00e9do Durand, George Drettakis, and Adrien Bousseau. 2019. Flexible SVBRDF capture with a multi-image deep network. Computer Graphics Forum(Eurographics Symposium on Rendering Conference Proceedings) 38, 4 (July2019), 13. Retrieved from http:\/\/www-sop.inria.fr\/reves\/Basilic\/2019\/DADDB19","journal-title":"Computer Graphics Forum(Eurographics Symposium on Rendering Conference Proceedings)"},{"issue":"4","key":"e_1_3_4_13_1","article-title":"Guided fine-tuning for large-scale material transfer","volume":"39","author":"Deschaintre Valentin","year":"2020","unstructured":"Valentin Deschaintre, George Drettakis, and Adrien Bousseau. 2020. Guided fine-tuning for large-scale material transfer. Computer Graphics Forum (Proceedings of the Eurographics Symposium on Rendering) 39, 4 (2020), 91\u2013105. Retrieved from http:\/\/www-sop.inria.fr\/reves\/Basilic\/2020\/DDB20","journal-title":"Computer Graphics Forum (Proceedings of the Eurographics Symposium on Rendering)"},{"key":"e_1_3_4_14_1","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","author":"Dhariwal Prafulla","year":"2021","unstructured":"Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat GANs on image synthesis. In Proceedings of the 35th International Conference on Neural Information Processing Systems. 8780\u20138794.","journal-title":"Proceedings of the 35th International Conference on Neural Information Processing Systems"},{"key":"e_1_3_4_15_1","article-title":"Generating images with perceptual similarity metrics based on deep networks","author":"Dosovitskiy Alexey","year":"2016","unstructured":"Alexey Dosovitskiy and Thomas Brox. 2016. Generating images with perceptual similarity metrics based on deep networks. In Proceedings of the 30th International Conference on Neural Information Processing Systems.","journal-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems"},{"key":"e_1_3_4_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01268"},{"issue":"4","key":"e_1_3_4_17_1","article-title":"Metappearance: Meta-learning for visual appearance reproduction","volume":"41","author":"Fischer Michael","year":"2022","unstructured":"Michael Fischer and Tobias Ritschel. 2022. Metappearance: Meta-learning for visual appearance reproduction. ACM Transactions on Graphics (Proc. SIGGRAPH Asia) 41, 4 (2022), 1\u201313.","journal-title":"ACM Transactions on Graphics (Proc. SIGGRAPH Asia)"},{"key":"e_1_3_4_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3306346.3323042"},{"key":"e_1_3_4_19_1","volume-title":"Advances in Neural Information Processing Systems","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems, Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K.Q. Weinberger (Eds.). Vol. 27, Curran Associates, Inc. Retrieved from https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2014\/file\/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf"},{"key":"e_1_3_4_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/3059330.3059335"},{"key":"e_1_3_4_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3528223.3530173"},{"key":"e_1_3_4_22_1","article-title":"Improved training of Wasserstein GANs","author":"Gulrajani Ishaan","year":"2017","unstructured":"Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C. Courville. 2017. Improved training of Wasserstein GANs. In Proceedings of the 31st International Conference on Neural Information Processing Systems.","journal-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems"},{"key":"e_1_3_4_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459854"},{"key":"e_1_3_4_24_1","doi-asserted-by":"publisher","unstructured":"Jie Guo Shuichang Lai Qinghao Tu Chengzhi Tao Changqing Zou and Yanwen Guo. 2023. Ultra-high resolution SVBRDF recovery from a single image. ACM Transactions on Graphics 42 3 (2023) 1\u201314. DOI:10.1145\/3593798Just Accepted.","DOI":"10.1145\/3593798"},{"key":"e_1_3_4_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3414685.3417779"},{"issue":"6","key":"e_1_3_4_26_1","article-title":"Generative modelling of BRDF textures from flash images","volume":"40","author":"Henzler Philipp","year":"2021","unstructured":"Philipp Henzler, Valentin Deschaintre, Niloy J. Mitra, and Tobias Ritschel. 2021. Generative modelling of BRDF textures from flash images. ACM Transactions on Graphics (Proc. SIGGRAPH Asia) 40, 6 (2021), 1\u201313.","journal-title":"ACM Transactions on Graphics (Proc. SIGGRAPH Asia)"},{"key":"e_1_3_4_27_1","doi-asserted-by":"crossref","unstructured":"Jack Hessel Ari Holtzman Maxwell Forbes Ronan Le Bras and Yejin Choi. 2021. Clipscore: A reference-free evaluation metric for image captioning. arXiv:2104.08718. Retrieved from https:\/\/arxiv.org\/abs\/2104.08718","DOI":"10.18653\/v1\/2021.emnlp-main.595"},{"key":"e_1_3_4_28_1","first-page":"6840","article-title":"Denoising diffusion probabilistic models","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. In Proceedings of the 34th International Conference on Neural Information Processing Systems. 6840\u20136851.","journal-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems"},{"key":"e_1_3_4_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/3586589.3586636"},{"key":"e_1_3_4_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356516"},{"key":"e_1_3_4_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3528233.3530733"},{"key":"e_1_3_4_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588432.3591520"},{"key":"e_1_3_4_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3502431"},{"key":"e_1_3_4_34_1","unstructured":"Qingqing Huang Daniel S. Park Tao Wang Timo I. Denk Andy Ly Nanxin Chen Zhengdong Zhang Zhishuai Zhang Jiahui Yu Christian Frank Jesse Engel Quoc V. Le William Chan Zhifeng Chen and Wei Han. 2023. Noise2Music: Text-conditioned Music Generation with Diffusion Models. arxiv:cs.SD\/2302.03917. Retrieved from https:\/\/arxiv.org\/abs\/2302.03917"},{"key":"e_1_3_4_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_3_4_36_1","unstructured":"\u00c1lvaro Barbero Jim\u00e9nez. 2023. Mixture of Diffusers for scene composition and high resolution image generation. arXiv:2302.02412. Retrieved from https:\/\/arxiv.org\/abs\/2302.02412"},{"issue":"3","key":"e_1_3_4_37_1","first-page":"1","article-title":"Real shading in unreal engine 4","volume":"4","author":"Karis Brian","year":"2013","unstructured":"Brian Karis. 2013. Real shading in unreal engine 4. Proc. Physically Based Shading Theory Practice 4, 3 (2013), 1.","journal-title":"Proc. Physically Based Shading Theory Practice"},{"key":"e_1_3_4_38_1","unstructured":"Tero Karras Timo Aila Samuli Laine and Jaakko Lehtinen. 2017. Progressive growing of GANs for improved quality stability and variation. arXiv:1710.10196. Retrieved from https:\/\/arxiv.org\/abs\/1710.10196"},{"key":"e_1_3_4_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"e_1_3_4_40_1","unstructured":"Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv:1312.6114. Retrieved from https:\/\/arxiv.org\/abs\/1312.6114"},{"key":"e_1_3_4_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073641"},{"key":"e_1_3_4_42_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14466"},{"key":"e_1_3_4_43_1","first-page":"75","volume-title":"Maps Common to Both Workflow","author":"McDermott Wes","year":"2018","unstructured":"Wes McDermott. 2018. Maps Common to Both Workflow. Allergorithmic, 75\u201379. Retrieved from https:\/\/substance3d.adobe.com\/tutorials\/courses\/the-pbr-guide-part-2"},{"key":"e_1_3_4_44_1","unstructured":"Lars Mescheder. 2018. On the convergence properties of GAN training. arXiv:1801.04406. Retrieved from https:\/\/arxiv.org\/abs\/1801.04406"},{"key":"e_1_3_4_45_1","unstructured":"Luke Metz Ben Poole David Pfau and Jascha Sohl-Dickstein. 2016. Unrolled generative adversarial networks. arXiv:1611.02163. Retrieved from https:\/\/arxiv.org\/abs\/1611.02163"},{"key":"e_1_3_4_46_1","first-page":"8748","volume-title":"Proceedings of the International Conference on Machine Learning","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning transferable visual models from natural language supervision. In Proceedings of the International Conference on Machine Learning. PMLR, 8748\u20138763."},{"key":"e_1_3_4_47_1","unstructured":"Aditya Ramesh Prafulla Dhariwal Alex Nichol Casey Chu and Mark Chen. 2022. Hierarchical text-conditional image generation with clip latents. arXiv:2204.06125. Retrieved from https:\/\/arxiv.org\/abs\/2204.06125"},{"key":"e_1_3_4_48_1","first-page":"1278","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Rezende Danilo Jimenez","year":"2014","unstructured":"Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra. 2014. Stochastic backpropagation and approximate inference in deep generative models. In Proceedings of the International Conference on Machine Learning. PMLR, 1278\u20131286."},{"key":"e_1_3_4_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"e_1_3_4_50_1","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/978-3-030-58520-4_38","volume-title":"Proceedings of the16th European Conference on Computer Vision\u2013ECCV 2020, Part XVII 16","author":"Rombach Robin","year":"2020","unstructured":"Robin Rombach, Patrick Esser, and Bj\u00f6rn Ommer. 2020a. Making sense of CNNs: Interpreting deep representations and their invariances with INNs. In Proceedings of the16th European Conference on Computer Vision\u2013ECCV 2020, Part XVII 16. Springer, 647\u2013664."},{"key":"e_1_3_4_51_1","first-page":"2784","article-title":"Network-to-network translation with conditional invertible neural networks","author":"Rombach Robin","year":"2020","unstructured":"Robin Rombach, Patrick Esser, and Bjorn Ommer. 2020b. Network-to-network translation with conditional invertible neural networks. In Proceedings of the 34th International Conference on Neural Information Processing Systems. 2784\u20132797.","journal-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems"},{"key":"e_1_3_4_52_1","first-page":"234","volume-title":"Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015, Part III 18","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015, Part III 18. Springer, 234\u2013241."},{"key":"e_1_3_4_53_1","unstructured":"Chitwan Saharia William Chan Saurabh Saxena Lala Lit Jay Whang Emily Denton Seyed Kamyar Seyed Ghasemipour Burcu Karagol Ayan S. Sara Mahdavi Raphael Gontijo-Lopes Tim Salimans Jonathan Ho David J. Fleet and Mohammad Norouzi. 2024. Photorealistic text-to-image diffusion models with deep language understanding. In Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS\u201922) Curran Associates Inc. New Orleans LA USA."},{"key":"e_1_3_4_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3610548.3618194"},{"issue":"6","key":"e_1_3_4_55_1","first-page":"196","article-title":"MATch: Differentiable material graphs for procedural material capture","volume":"39","author":"Shi Liang","year":"2020","unstructured":"Liang Shi, Beichen Li, Milo\u0161 Ha\u0161an, Kalyan Sunkavalli, Tamy Boubekeur, Radomir Mech, and Wojciech Matusik. 2020. MATch: Differentiable material graphs for procedural material capture. ACM Transactions on Graphics 39, 6, Article 196 (Dec.2020), 15 pages.","journal-title":"ACM Transactions on Graphics"},{"key":"e_1_3_4_56_1","first-page":"2256","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Sohl-Dickstein Jascha","year":"2015","unstructured":"Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of the International Conference on Machine Learning. PMLR, 2256\u20132265."},{"key":"e_1_3_4_57_1","unstructured":"Jiaming Song Chenlin Meng and Stefano Ermon. 2020. Denoising diffusion implicit models. arXiv:2010.02502. Retrieved from https:\/\/arxiv.org\/abs\/2010.02502"},{"key":"e_1_3_4_58_1","unstructured":"Yang Song Prafulla Dhariwal Mark Chen and Ilya Sutskever. 2023. Consistency models. arxiv:cs.LG\/2303.01469. Retrieved from https:\/\/arxiv.org\/abs\/2303.01469"},{"key":"e_1_3_4_59_1","unstructured":"Aaron van den Oord Oriol Vinyals and Koray Kavukcuoglu. 2017. Neural discrete representation learning. In Proceedings of the 31st International Conference on Neural Information Processing Systems."},{"key":"e_1_3_4_60_1","article-title":"Attention is all you need","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. InProceedings of the 31st International Conference on Neural Information Processing Systems.","journal-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems"},{"key":"e_1_3_4_61_1","first-page":"22109","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR\u201924)","author":"Vecchio Giuseppe","year":"2024","unstructured":"Giuseppe Vecchio and Valentin Deschaintre. 2024. MatSynth: A modern PBR materials dataset. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR\u201924). 22109\u201322118."},{"key":"e_1_3_4_62_1","first-page":"12840","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Vecchio Giuseppe","year":"2021","unstructured":"Giuseppe Vecchio, Simone Palazzo, and Concetto Spampinato. 2021. SurfaceNet: Adversarial SVBRDF estimation from a single image. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 12840\u201312848."},{"key":"e_1_3_4_63_1","first-page":"4429","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR\u201924)","author":"Vecchio Giuseppe","year":"2024","unstructured":"Giuseppe Vecchio, Renato Sortino, Simone Palazzo, and Concetto Spampinato. 2024. MatFuse: Controllable material generation with diffusion models. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR\u201924). 4429\u20134438."},{"key":"e_1_3_4_64_1","doi-asserted-by":"publisher","DOI":"10.5555\/2383847.2383874"},{"key":"e_1_3_4_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"e_1_3_4_66_1","article-title":"DiffMat: Latent diffusion models for image-guided material generation","author":"Yuan Liang","year":"2024","unstructured":"Liang Yuan, Dingkun Yan, Suguru Saito, and Issei Fujishiro. 2024. DiffMat: Latent diffusion models for image-guided material generation. Visual Informatics 8, 1 (2024), 6\u201314.","journal-title":"Visual Informatics"},{"key":"e_1_3_4_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00475"},{"key":"e_1_3_4_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"e_1_3_4_69_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"e_1_3_4_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588432.3591535"},{"key":"e_1_3_4_71_1","doi-asserted-by":"publisher","DOI":"10.1145\/3550469.3555403"},{"key":"e_1_3_4_72_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14781"},{"key":"e_1_3_4_73_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.142635"},{"key":"e_1_3_4_74_1","doi-asserted-by":"publisher","DOI":"10.1145\/3550454.3555495"}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3688830","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3688830","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:10Z","timestamp":1750291450000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3688830"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,11]]},"references-count":73,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10,31]]}},"alternative-id":["10.1145\/3688830"],"URL":"https:\/\/doi.org\/10.1145\/3688830","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,11]]},"assertion":[{"value":"2023-09-20","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-07-29","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-09-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}