{
    "status": "ok",
    "message-type": "work",
    "message-version": "1.0.0",
    "message": {
        "indexed": {
            "date-parts": [
                [
                    2020,
                    4,
                    4
                ]
            ],
            "date-time": "2020-04-04T01:11:55Z",
            "timestamp": 1585962715184
        },
        "reference-count": 60,
        "publisher": "Association for Computing Machinery (ACM)",
        "issue": "4",
        "license": [
            {
                "URL": "http://www.acm.org/publications/policies/copyright_policy#Background",
                "start": {
                    "date-parts": [
                        [
                            2018,
                            7,
                            30
                        ]
                    ],
                    "date-time": "2018-07-30T00:00:00Z",
                    "timestamp": 1532908800000
                },
                "delay-in-days": 0,
                "content-version": "vor"
            }
        ],
        "funder": [
            {
                "name": "ERC",
                "award": []
            }
        ],
        "content-domain": {
            "domain": [],
            "crossmark-restriction": false
        },
        "short-container-title": [
            "ACM Trans. Graph."
        ],
        "published-print": {
            "date-parts": [
                [
                    2018,
                    8,
                    10
                ]
            ]
        },
        "DOI": "10.1145/3197517.3201304",
        "type": "journal-article",
        "created": {
            "date-parts": [
                [
                    2018,
                    7,
                    31
                ]
            ],
            "date-time": "2018-07-31T15:56:23Z",
            "timestamp": 1533052583000
        },
        "page": "1-15",
        "source": "Crossref",
        "is-referenced-by-count": 20,
        "title": [
            "tempoGAN"
        ],
        "prefix": "10.1145",
        "volume": "37",
        "author": [
            {
                "given": "You",
                "family": "Xie",
                "sequence": "first",
                "affiliation": [
                    {
                        "name": "Technical University of Munich"
                    }
                ]
            },
            {
                "given": "Erik",
                "family": "Franz",
                "sequence": "additional",
                "affiliation": [
                    {
                        "name": "Technical University of Munich"
                    }
                ]
            },
            {
                "given": "Mengyu",
                "family": "Chu",
                "sequence": "additional",
                "affiliation": [
                    {
                        "name": "Technical University of Munich"
                    }
                ]
            },
            {
                "given": "Nils",
                "family": "Thuerey",
                "sequence": "additional",
                "affiliation": [
                    {
                        "name": "Technical University of Munich"
                    }
                ]
            }
        ],
        "member": "320",
        "reference": [
            {
                "key": "e_1_2_2_1_1",
                "unstructured": "Martin Arjovsky Soumith Chintala and L\u00e9on Bottou. 2017. Wasserstein GAN. arXiv:1701.07875 (2017).  Martin Arjovsky Soumith Chintala and L\u00e9on Bottou. 2017. Wasserstein GAN. arXiv:1701.07875 (2017)."
            },
            {
                "key": "e_1_2_2_2_1",
                "DOI": "10.1145/3072959.3073708",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_3_1",
                "unstructured": "David Berthelot Tom Schumm and Luke Metz. 2017. BeGAN: Boundary equilibrium generative adversarial networks. arXiv:1703.10717 (2017).  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.  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",
                "unstructured": "Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York Inc. Secaucus NJ USA.   Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York Inc. Secaucus NJ USA."
            },
            {
                "key": "e_1_2_2_6_1",
                "DOI": "10.1145/3072959.3073601",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_7_1",
                "author": "Chen Dongdong",
                "year": "2017",
                "volume-title": "Coherent Online Video Style Transfer. In The IEEE International Conference on Computer Vision (ICCV)."
            },
            {
                "key": "e_1_2_2_8_1",
                "DOI": "10.1145/3072959.3073643",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_9_1",
                "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).  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",
                "unstructured": "Chao Dong Chen Change Loy Kaiming He and Xiaoou Tang. 2016. Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence 38 2 (2016) 295--307. 10.1109/TPAMI.2015.2439281   Chao Dong Chen Change Loy Kaiming He and Xiaoou Tang. 2016. Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence 38 2 (2016) 295--307. 10.1109/TPAMI.2015.2439281"
            },
            {
                "key": "e_1_2_2_11_1",
                "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.   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."
            },
            {
                "key": "e_1_2_2_12_1",
                "DOI": "10.1109/TPAMI.2015.2496141",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_13_1",
                "unstructured": "Amir Barati Farimani Joseph Gomes and Vijay S Pande. 2017. Deep Learning the Physics of Transport Phenomena. arXiv:1709.02432 (2017).  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",
                "author": "Flynn John",
                "year": "2016",
                "volume-title": "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5515--5524"
            },
            {
                "key": "e_1_2_2_15_1",
                "DOI": "10.1109/CVPR.2014.81",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_16_1",
                "author": "Goodfellow Ian",
                "year": "2016",
                "volume-title": "NIPS 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701"
            },
            {
                "key": "e_1_2_2_17_1",
                "unstructured": "Ian Goodfellow Yoshua Bengio and Aaron Courville. 2016. Deep Learning. MIT Press.   Ian Goodfellow Yoshua Bengio and Aaron Courville. 2016. Deep Learning. MIT Press."
            },
            {
                "key": "e_1_2_2_18_1",
                "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.  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": "10.1109/CVPR.2017.632",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_20_1",
                "DOI": "10.1007/978-3-319-46475-6_43",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_21_1",
                "DOI": "10.1145/3130800.3130880",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_22_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 (2017).  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": "10.1145/1964921.1964988",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_24_1",
                "author": "Kim Byungmoon",
                "year": "2005",
                "volume-title": "Proceedings of the First Eurographics conference on Natural Phenomena. 51--56"
            },
            {
                "key": "e_1_2_2_25_1",
                "DOI": "10.1109/CVPR.2016.182",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "#cr-split#-e_1_2_2_26_1.1",
                "unstructured": "Theodore Kim Nils Thuerey Doug James and Markus Gross. 2008. Wavelet Turbulence for Fluid Simulation. ACM Trans. Graph. 27",
                "DOI": "10.1145/1360612.1360649",
                "doi-asserted-by": "crossref"
            },
            {
                "key": "#cr-split#-e_1_2_2_26_1.2",
                "unstructured": "(3) (2008) 50:1--6. 10.1145/1360612.1360649 Theodore Kim Nils Thuerey Doug James and Markus Gross. 2008. Wavelet Turbulence for Fluid Simulation. ACM Trans. Graph. 27"
            },
            {
                "key": "#cr-split#-e_1_2_2_26_1.3",
                "unstructured": "(3) (2008) 50:1--6. 10.1145/1360612.1360649"
            },
            {
                "key": "e_1_2_2_27_1",
                "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.   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."
            },
            {
                "key": "e_1_2_2_28_1",
                "DOI": "10.1145/2816795.2818129",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_29_1",
                "unstructured": "Christian Ledig Lucas Theis Ferenc Husz\u00e1r Jose Caballero Andrew Cunningham Alejandro Acosta Andrew Aitken Alykhan Tejani Johannes Totz Zehan Wang etal 2016. Photo-realistic single image super-resolution using a generative adversarial network. arXiv:1609.04802 (2016).  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",
                "doi-asserted-by": "crossref"
            },
            {
                "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",
                "journal-title": "Proc. of IEEE Comp. Vision and Pattern Rec."
            },
            {
                "key": "e_1_2_2_31_1",
                "author": "Liu Ding",
                "year": "2017",
                "volume-title": "Robust Video Super-Resolution With Learned Temporal Dynamics. In The IEEE International Conference on Computer Vision (ICCV)."
            },
            {
                "key": "e_1_2_2_32_1",
                "unstructured": "Zichao Long Yiping Lu Xianzhong Ma and Bin Dong. 2017. PDE-Net: Learning PDEs from Data. arXiv:1710.09668 (2017).  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",
                "unstructured": "Fujun Luan Sylvain Paris Eli Shechtman and Kavita Bala. 2017. Deep Photo Style Transfer. arXiv preprint arXiv:1703.07511 (2017).  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",
                "unstructured": "W Magnus F Henrik A Chris and M Stephen. 2011. Capturing Thin Features in Smoke Simulations. Siggraph Talk (2011).  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",
                "unstructured": "Michael Mathieu Camille Couprie and Yann LeCun. 2015. Deep multi-scale video prediction beyond mean square error. arXiv preprint arXiv:1511.05440 (2015).  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": "10.1145/1015706.1015744",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_37_1",
                "unstructured": "Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014).  Mehdi Mirza and Simon Osindero. 2014. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784 (2014)."
            },
            {
                "key": "e_1_2_2_38_1",
                "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).  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": "10.1145/1409060.1409119",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_40_1",
                "unstructured": "Augustus Odena Vincent Dumoulin and Chris Olah. 2016. Deconvolution and Checker-board Artifacts. Distill (2016).  Augustus Odena Vincent Dumoulin and Chris Olah. 2016. Deconvolution and Checker-board Artifacts. Distill (2016)."
            },
            {
                "key": "e_1_2_2_41_1",
                "DOI": "10.1145/2508363.2508429",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_42_1",
                "DOI": "10.1145/3072959.3073602",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_43_1",
                "unstructured": "Lukas Prantl Boris Bonev and Nils Thuerey. 2017. Pre-computed Liquid Spaces with Generative Neural Networks and Optical Flow. arXiv:1704.07854 (2017).  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",
                "author": "Radford Alec",
                "year": "2016",
                "volume-title": "Proc. ICLR"
            },
            {
                "key": "e_1_2_2_45_1",
                "DOI": "10.1145/882262.882335",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_46_1",
                "DOI": "10.1007/978-3-319-24574-4_28",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_47_1",
                "author": "Ruder Manuel",
                "year": "2016",
                "volume-title": "GCPR 2016, Hannover, Germany, September 12-15, 2016, Proceedings. 26--36"
            },
            {
                "key": "e_1_2_2_48_1",
                "DOI": "10.1109/ICCV.2017.308",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_49_1",
                "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.   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."
            },
            {
                "key": "e_1_2_2_50_1",
                "DOI": "10.5555/1632592.1632594",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_51_1",
                "DOI": "10.1007/s10915-007-9166-4",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_52_1",
                "unstructured": "Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv.1409.1556 (2014).  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": "10.1145/311535.311548",
                "doi-asserted-by": "publisher"
            },
            {
                "key": "e_1_2_2_54_1",
                "unstructured": "Jonathan Tompson Kristofer Schlachter Pablo Sprechmann and Ken Perlin. 2016. Accelerating Eulerian Fluid Simulation With Convolutional Networks. arXiv:1607.03597 (2016).  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",
                "unstructured": "Kiwon Um Xiangyu Hu and Nils Thuerey. 2017. Splash Modeling with Neural Networks. arXiv:1704.04456 (2017).  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.  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",
                "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).  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",
                "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).  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": "http://dl.acm.org/ft_gateway.cfm?id=3201304&ftid=1991706&dwn=1",
                "content-type": "unspecified",
                "content-version": "vor",
                "intended-application": "similarity-checking"
            }
        ],
        "deposited": {
            "date-parts": [
                [
                    2020,
                    4,
                    4
                ]
            ],
            "date-time": "2020-04-04T00:50:25Z",
            "timestamp": 1585961425000
        },
        "score": 1.0,
        "subtitle": [
            "a temporally coherent, volumetric GAN for super-resolution fluid flow"
        ],
        "short-title": [],
        "issued": {
            "date-parts": [
                [
                    2018,
                    8,
                    10
                ]
            ]
        },
        "references-count": 60,
        "journal-issue": {
            "published-print": {
                "date-parts": [
                    [
                        2018,
                        8,
                        10
                    ]
                ]
            },
            "issue": "4"
        },
        "alternative-id": [
            "10.1145/3197517.3201304"
        ],
        "URL": "http://dx.doi.org/10.1145/3197517.3201304",
        "relation": {
            "cites": []
        },
        "ISSN": [
            "0730-0301",
            "1557-7368"
        ],
        "issn-type": [
            {
                "value": "0730-0301",
                "type": "print"
            },
            {
                "value": "1557-7368",
                "type": "electronic"
            }
        ]
    }
}