{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T19:50:51Z","timestamp":1787169051810,"version":"3.56.0"},"reference-count":58,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2018,7,30]],"date-time":"2018-07-30T00:00:00Z","timestamp":1532908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"ERC"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2018,8,31]]},"abstract":"<jats:p>We propose a temporally coherent generative model addressing the super-resolution problem for fluid flows. Our work represents a first approach to synthesize four-dimensional physics fields with neural networks. Based on a conditional generative adversarial network that is designed for the inference of three-dimensional volumetric data, our model generates consistent and detailed results by using a novel temporal discriminator, in addition to the commonly used spatial one. Our experiments show that the generator is able to infer more realistic high-resolution details by using additional physical quantities, such as low-resolution velocities or vorticities. Besides improvements in the training process and in the generated outputs, these inputs offer means for artistic control as well. We additionally employ a physics-aware data augmentation step, which is crucial to avoid overfitting and to reduce memory requirements. In this way, our network learns to generate adverted quantities with highly detailed, realistic, and temporally coherent features. Our method works instantaneously, using only a single time-step of low-resolution fluid data. We demonstrate the abilities of our method using a variety of complex inputs and applications in two and three dimensions.<\/jats:p>","DOI":"10.1145\/3197517.3201304","type":"journal-article","created":{"date-parts":[[2018,7,31]],"date-time":"2018-07-31T11:56:23Z","timestamp":1533038183000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":230,"title":["tempoGAN"],"prefix":"10.1145","volume":"37","author":[{"given":"You","family":"Xie","sequence":"first","affiliation":[{"name":"Technical University of Munich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aleksandra","family":"Franz","sequence":"additional","affiliation":[{"name":"Technical University of Munich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengyu","family":"Chu","sequence":"additional","affiliation":[{"name":"Technical University of Munich"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nils","family":"Thuerey","sequence":"additional","affiliation":[{"name":"Technical University of Munich"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,7,30]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"arXiv:1701.07875","author":"Arjovsky Martin","year":"2017","unstructured":"Martin Arjovsky, Soumith Chintala, and L\u00e9on Bottou. 2017. Wasserstein GAN. arXiv:1701.07875 (2017)."},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073708"},{"key":"e_1_2_2_3_1","volume-title":"BeGAN: Boundary equilibrium generative adversarial networks. arXiv:1703.10717","author":"Berthelot David","year":"2017","unstructured":"David Berthelot, Tom Schumm, and Luke Metz. 2017. BeGAN: Boundary equilibrium generative adversarial networks. arXiv:1703.10717 (2017)."},{"key":"e_1_2_2_4_1","unstructured":"Prateep Bhattacharjee and Sukhendu Das. 2017. Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks. In Advances in Neural Information Processing Systems. 4271--4280."},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.5555\/1162264"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073601"},{"key":"e_1_2_2_7_1","volume-title":"Coherent Online Video Style Transfer. In The IEEE International Conference on Computer Vision (ICCV).","author":"Chen Dongdong","year":"2017","unstructured":"Dongdong Chen, Jing Liao, Lu Yuan, Nenghai Yu, and Gang Hua. 2017. Coherent Online Video Style Transfer. In The IEEE International Conference on Computer Vision (ICCV)."},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073643"},{"key":"e_1_2_2_9_1","volume-title":"Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge. arXiv preprint arXiv:1711.07970","author":"de Bezenac Emmanuel","year":"2017","unstructured":"Emmanuel de Bezenac, Arthur Pajot, and Patrick Gallinari. 2017. Deep Learning for Physical Processes: Incorporating Prior Scientific Knowledge. arXiv preprint arXiv:1711.07970 (2017)."},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","unstructured":"Alexey Dosovitskiy and Thomas Brox. 2016. Generating images with perceptual similarity metrics based on deep networks. In Advances in Neural Information Processing Systems. 658--666.","DOI":"10.5555\/3157096.3157170"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2496141"},{"key":"e_1_2_2_13_1","volume-title":"Deep Learning the Physics of Transport Phenomena. arXiv:1709.02432","author":"Farimani Amir Barati","year":"2017","unstructured":"Amir Barati Farimani, Joseph Gomes, and Vijay S Pande. 2017. Deep Learning the Physics of Transport Phenomena. arXiv:1709.02432 (2017)."},{"key":"e_1_2_2_14_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5515--5524","author":"Flynn John","year":"2016","unstructured":"John Flynn, Ivan Neulander, James Philbin, and Noah Snavely. 2016. DeepStereo: Learning to predict new views from the world's imagery. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5515--5524."},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"e_1_2_2_16_1","volume-title":"NIPS 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701","author":"Goodfellow Ian","year":"2016","unstructured":"Ian Goodfellow. 2016. NIPS 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160 (2016)."},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/3086952"},{"key":"e_1_2_2_18_1","volume-title":"Generative Adversarial Nets. stat 1050","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative Adversarial Nets. stat 1050 (2014), 10."},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3130800.3130880"},{"key":"e_1_2_2_22_1","volume-title":"Progressive growing of gans for improved quality, stability, and variation. arXiv:1710.10196","author":"Karras Tero","year":"2017","unstructured":"Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. 2017. Progressive growing of gans for improved quality, stability, and variation. arXiv:1710.10196 (2017)."},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1964921.1964988"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.5555\/2381356.2381364"},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.182"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/1360612.1360649"},{"key":"e_1_2_2_27_1","doi-asserted-by":"publisher","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems. NIPS 1097--1105.","DOI":"10.5555\/2999134.2999257"},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2816795.2818129"},{"key":"e_1_2_2_29_1","doi-asserted-by":"crossref","unstructured":"Christian Ledig Lucas Theis Ferenc Husz\u00e1r Jose Caballero Andrew Cunningham Alejandro Acosta Andrew Aitken Alykhan Tejani Johannes Totz Zehan Wang et al. 2016. Photo-realistic single image super-resolution using a generative adversarial network. arXiv:1609.04802 (2016).","DOI":"10.1109\/CVPR.2017.19"},{"key":"e_1_2_2_30_1","first-page":"3","article-title":"Enhanced deep residual networks for single image super-resolution","volume":"1","author":"Lim Bee","year":"2017","unstructured":"Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee. 2017. Enhanced deep residual networks for single image super-resolution. In Proc. of IEEE Comp. Vision and Pattern Rec., Vol. 1. 3.","journal-title":"Proc. of IEEE Comp. Vision and Pattern Rec."},{"key":"e_1_2_2_31_1","volume-title":"Robust Video Super-Resolution With Learned Temporal Dynamics. In The IEEE International Conference on Computer Vision (ICCV).","author":"Liu Ding","year":"2017","unstructured":"Ding Liu, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, and Thomas Huang. 2017. Robust Video Super-Resolution With Learned Temporal Dynamics. In The IEEE International Conference on Computer Vision (ICCV)."},{"key":"e_1_2_2_32_1","volume-title":"PDE-Net: Learning PDEs from Data. arXiv:1710.09668","author":"Long Zichao","year":"2017","unstructured":"Zichao Long, Yiping Lu, Xianzhong Ma, and Bin Dong. 2017. PDE-Net: Learning PDEs from Data. arXiv:1710.09668 (2017)."},{"key":"e_1_2_2_33_1","volume-title":"Deep Photo Style Transfer. arXiv preprint arXiv:1703.07511","author":"Luan Fujun","year":"2017","unstructured":"Fujun Luan, Sylvain Paris, Eli Shechtman, and Kavita Bala. 2017. Deep Photo Style Transfer. arXiv preprint arXiv:1703.07511 (2017)."},{"key":"e_1_2_2_34_1","volume-title":"Capturing Thin Features in Smoke Simulations. Siggraph Talk","author":"Magnus W","year":"2011","unstructured":"W Magnus, F Henrik, A Chris, and M Stephen. 2011. Capturing Thin Features in Smoke Simulations. Siggraph Talk (2011)."},{"key":"e_1_2_2_35_1","volume-title":"Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440","author":"Mathieu Michael","year":"2015","unstructured":"Michael Mathieu, Camille Couprie, and Yann LeCun. 2015. Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440 (2015)."},{"key":"e_1_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/1015706.1015744"},{"key":"e_1_2_2_37_1","volume-title":"Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784","author":"Mirza Mehdi","year":"2014","unstructured":"Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)."},{"key":"e_1_2_2_38_1","volume-title":"Reconstruction of three-dimensional porous media using generative adversarial neural networks. arXiv:1704.03225","author":"Mosser Lukas","year":"2017","unstructured":"Lukas Mosser, Olivier Dubrule, and Martin J Blunt. 2017. Reconstruction of three-dimensional porous media using generative adversarial neural networks. arXiv:1704.03225 (2017)."},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409060.1409119"},{"key":"e_1_2_2_40_1","volume-title":"Deconvolution and Checker-board Artifacts. Distill","author":"Odena Augustus","year":"2016","unstructured":"Augustus Odena, Vincent Dumoulin, and Chris Olah. 2016. Deconvolution and Checker-board Artifacts. Distill (2016)."},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2508363.2508429"},{"key":"e_1_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3072959.3073602"},{"key":"e_1_2_2_43_1","volume-title":"Pre-computed Liquid Spaces with Generative Neural Networks and Optical Flow. arXiv:1704.07854","author":"Prantl Lukas","year":"2017","unstructured":"Lukas Prantl, Boris Bonev, and Nils Thuerey. 2017. Pre-computed Liquid Spaces with Generative Neural Networks and Optical Flow. arXiv:1704.07854 (2017)."},{"key":"e_1_2_2_44_1","volume-title":"Proc. ICLR","author":"Radford Alec","year":"2016","unstructured":"Alec Radford, Luke Metz, and Soumith Chintala. 2016. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Proc. ICLR (2016)."},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/882262.882335"},{"key":"e_1_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_2_47_1","volume-title":"GCPR 2016, Hannover, Germany, September 12-15, 2016, Proceedings. 26--36","author":"Ruder Manuel","year":"2016","unstructured":"Manuel Ruder, Alexey Dosovitskiy, and Thomas Brox. 2016. Artistic Style Transfer for Videos. In Pattern Recognition - 38th German Conference, GCPR 2016, Hannover, Germany, September 12-15, 2016, Proceedings. 26--36."},{"key":"e_1_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.308"},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","unstructured":"Tim Salimans Ian Goodfellow Wojciech Zaremba Vicki Cheung Alec Radford and Xi Chen. 2016. Improved techniques for training gans. In Advances in Neural Information Processing Systems. 2234--2242.","DOI":"10.5555\/3157096.3157346"},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.5555\/1632592.1632594"},{"key":"e_1_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10915-007-9166-4"},{"key":"e_1_2_2_52_1","volume-title":"Very deep convolutional networks for large-scale image recognition. arXiv.1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv.1409.1556 (2014)."},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/311535.311548"},{"key":"e_1_2_2_54_1","volume-title":"Accelerating Eulerian Fluid Simulation With Convolutional Networks. arXiv:1607.03597","author":"Tompson Jonathan","year":"2016","unstructured":"Jonathan Tompson, Kristofer Schlachter, Pablo Sprechmann, and Ken Perlin. 2016. Accelerating Eulerian Fluid Simulation With Convolutional Networks. arXiv:1607.03597 (2016)."},{"key":"e_1_2_2_55_1","volume-title":"Splash Modeling with Neural Networks. arXiv:1704.04456","author":"Um Kiwon","year":"2017","unstructured":"Kiwon Um, Xiangyu Hu, and Nils Thuerey. 2017. Splash Modeling with Neural Networks. arXiv:1704.04456 (2017)."},{"key":"e_1_2_2_56_1","unstructured":"Lantao Yu Weinan Zhang Jun Wang and Yong Yu. 2017. SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient.. In AAAI. 2852--2858."},{"key":"e_1_2_2_57_1","volume-title":"Loss Functions for Neural Networks for Image Processing. arXiv preprint arXiv:1511.08861","author":"Zhao Hang","year":"2015","unstructured":"Hang Zhao, Orazio Gallo, Iuri Frosio, and Jan Kautz. 2015. Loss Functions for Neural Networks for Image Processing. arXiv preprint arXiv:1511.08861 (2015)."},{"key":"e_1_2_2_58_1","volume-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv:1703.10593","author":"Zhu Jun-Yan","year":"2017","unstructured":"Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv:1703.10593 (2017)."}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3197517.3201304","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3197517.3201304","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T18:12:49Z","timestamp":1755540769000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3197517.3201304"}},"subtitle":["a temporally coherent, volumetric GAN for super-resolution fluid flow"],"short-title":[],"issued":{"date-parts":[[2018,7,30]]},"references-count":58,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2018,8,31]]}},"alternative-id":["10.1145\/3197517.3201304"],"URL":"https:\/\/doi.org\/10.1145\/3197517.3201304","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,30]]},"assertion":[{"value":"2018-07-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}