{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T17:00:52Z","timestamp":1777568452422,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":80,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Natural Science Foundation of China","award":["62272245"],"award-info":[{"award-number":["62272245"]}]},{"name":"Natural Science Foundation of China","award":["62132012"],"award-info":[{"award-number":["62132012"]}]},{"name":"Fundamental Research Funds for the Central Universities of Nankai University","award":["63243147"],"award-info":[{"award-number":["63243147"]}]},{"name":"Tianjin science and technology projects","award":["22JCYBJC01270"],"award-info":[{"award-number":["22JCYBJC01270"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,12,3]]},"DOI":"10.1145\/3680528.3687667","type":"proceedings-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T08:14:37Z","timestamp":1733213677000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["NeuSmoke: Efficient Smoke Reconstruction and View Synthesis with Neural Transportation Fields"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6065-7296","authenticated-orcid":false,"given":"Jiaxiong","family":"Qiu","sequence":"first","affiliation":[{"name":"TMCC, College of Computer Science, Nankai University, Tianjin, China and Robot Lab, Horizon Robotics, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0987-3401","authenticated-orcid":false,"given":"Ruihong","family":"Cen","sequence":"additional","affiliation":[{"name":"TMCC, College of Computer Science, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7416-1216","authenticated-orcid":false,"given":"Zhong","family":"Li","sequence":"additional","affiliation":[{"name":"OPPO US Research Center, Silicon Valley, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6641-9383","authenticated-orcid":false,"given":"Han","family":"Yan","sequence":"additional","affiliation":[{"name":"TMCC, College of Computer Science, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5550-8758","authenticated-orcid":false,"given":"Ming-Ming","family":"Cheng","sequence":"additional","affiliation":[{"name":"TMCC, College of Computer Science, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8179-9122","authenticated-orcid":false,"given":"Bo","family":"Ren","sequence":"additional","affiliation":[{"name":"TMCC, College of Computer Science, Nankai University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"e_1_3_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01594"},{"key":"e_1_3_3_2_3_1","unstructured":"Benjamin Attal Eliot Laidlaw Aaron Gokaslan Changil Kim Christian Richardt James Tompkin and Matthew O\u2019Toole. 2021. T\u00f6rf: Time-of-flight radiance fields for dynamic scene view synthesis. Advances in neural information processing systems 34 (2021) 26289\u201326301."},{"key":"e_1_3_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12231"},{"key":"e_1_3_3_2_5_1","doi-asserted-by":"crossref","unstructured":"Peter Bauer Alan Thorpe and Gilbert Brunet. 2015. The quiet revolution of numerical weather prediction. Nature 525 7567 (2015) 47\u201355.","DOI":"10.1038\/nature14956"},{"key":"e_1_3_3_2_6_1","doi-asserted-by":"crossref","unstructured":"Jens Berg and Kaj Nystr\u00f6m. 2018. A unified deep artificial neural network approach to partial differential equations in complex geometries. Neurocomputing 317 (2018) 28\u201341.","DOI":"10.1016\/j.neucom.2018.06.056"},{"key":"e_1_3_3_2_7_1","unstructured":"James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization. Journal of machine learning research 13 2 (2012)."},{"key":"e_1_3_3_2_8_1","doi-asserted-by":"crossref","unstructured":"Kaifeng Bi Lingxi Xie Hengheng Zhang Xin Chen Xiaotao Gu and Qi Tian. 2023. Accurate medium-range global weather forecasting with 3D neural networks. Nature 619 7970 (2023) 533\u2013538.","DOI":"10.1038\/s41586-023-06185-3"},{"key":"e_1_3_3_2_9_1","doi-asserted-by":"crossref","unstructured":"Dennis\u00a0M Bushnell and KJ Moore. 1991. Drag reduction in nature. Annual review of fluid mechanics 23 1 (1991) 65\u201379.","DOI":"10.1146\/annurev.fluid.23.1.65"},{"key":"e_1_3_3_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00021"},{"key":"e_1_3_3_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01565"},{"key":"e_1_3_3_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01131"},{"key":"e_1_3_3_2_13_1","doi-asserted-by":"crossref","unstructured":"Alexandre\u00a0Joel Chorin. 1968. Numerical solution of the Navier-Stokes equations. Mathematics of computation 22 104 (1968) 745\u2013762.","DOI":"10.1090\/S0025-5718-1968-0242392-2"},{"key":"e_1_3_3_2_14_1","doi-asserted-by":"crossref","unstructured":"Mengyu Chu Lingjie Liu Quan Zheng Erik Franz Hans-Peter Seidel Christian Theobalt and Rhaleb Zayer. 2022. Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data. ACM Transactions on Graphics 41 4 Article 119 (aug 2022) 119:1-119:14\u00a0pages.","DOI":"10.1145\/3528223.3530169"},{"key":"e_1_3_3_2_15_1","doi-asserted-by":"crossref","unstructured":"Mengyu Chu Nils Thuerey Hans-Peter Seidel Christian Theobalt and Rhaleb Zayer. 2021. Learning meaningful controls for fluids. ACM Transactions on Graphics (TOG) 40 4 (2021) 1\u201313.","DOI":"10.1145\/3476576.3476661"},{"key":"e_1_3_3_2_16_1","doi-asserted-by":"crossref","unstructured":"Yitong Deng Hong-Xing Yu Diyang Zhang Jiajun Wu and Bo Zhu. 2023. Fluid Simulation on Neural Flow Maps. ACM Transactions on Graphics (TOG) 42 6 (2023) 1\u201321.","DOI":"10.1145\/3618392"},{"key":"e_1_3_3_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/1280720.1280780"},{"key":"e_1_3_3_2_18_1","doi-asserted-by":"publisher","unstructured":"Tao Du Kui Wu Pingchuan Ma Sebastien Wah Andrew Spielberg Daniela Rus and Wojciech Matusik. 2021a. DiffPD: Differentiable Projective Dynamics. ACM Trans. Graph. 41 2 Article 13 (nov 2021) 21\u00a0pages. 10.1145\/3490168https:\/\/dl.acm.org\/doi\/10.1145\/3490168","DOI":"10.1145\/3490168"},{"key":"e_1_3_3_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01406"},{"key":"e_1_3_3_2_20_1","doi-asserted-by":"crossref","unstructured":"Marie-Lena Eckert Kiwon Um and Nils Thuerey. 2019. ScalarFlow: a large-scale volumetric data set of real-world scalar transport flows for computer animation and machine learning. ACM Transactions on Graphics (TOG) 38 6 (2019) 1\u201316.","DOI":"10.1145\/3355089.3356545"},{"key":"e_1_3_3_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3550469.3555383"},{"key":"e_1_3_3_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/258734.258838"},{"key":"e_1_3_3_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00168"},{"key":"e_1_3_3_2_24_1","volume-title":"The Eleventh International Conference on Learning Representations","author":"Franz Erik","year":"2022","unstructured":"Erik Franz, Barbara Solenthaler, and Nils Thuerey. 2022. Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_3_2_25_1","unstructured":"Wanshui Gan Hongbin Xu Yi Huang Shifeng Chen and Naoto Yokoya. 2023. V4d: Voxel for 4d novel view synthesis. IEEE Transactions on Visualization and Computer Graphics (2023)."},{"key":"e_1_3_3_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00566"},{"key":"e_1_3_3_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00966"},{"key":"e_1_3_3_2_28_1","doi-asserted-by":"crossref","unstructured":"Zhenglin Geng Daniel Johnson and Ronald Fedkiw. 2020. Coercing machine learning to output physically accurate results. J. Comput. Phys. 406 (2020) 109099.","DOI":"10.1016\/j.jcp.2019.109099"},{"key":"e_1_3_3_2_29_1","doi-asserted-by":"crossref","unstructured":"Frederic Gibou David Hyde and Ron Fedkiw. 2019. Sharp interface approaches and deep learning techniques for multiphase flows. J. Comput. Phys. 380 (2019) 442\u2013463.","DOI":"10.1016\/j.jcp.2018.05.031"},{"key":"e_1_3_3_2_30_1","doi-asserted-by":"crossref","unstructured":"James Gregson Ivo Ihrke Nils Thuerey and Wolfgang Heidrich. 2014. From capture to simulation: connecting forward and inverse problems in fluids. ACM Transactions on Graphics (TOG) 33 4 (2014) 1\u201311.","DOI":"10.1145\/2601097.2601147"},{"key":"e_1_3_3_2_31_1","doi-asserted-by":"crossref","unstructured":"Shouling He Konrad Reif and Rolf Unbehauen. 2000. Multilayer neural networks for solving a class of partial differential equations. Neural networks 13 3 (2000) 385\u2013396.","DOI":"10.1016\/S0893-6080(00)00013-7"},{"key":"e_1_3_3_2_32_1","unstructured":"Jonathan Ho Ajay Jain and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in neural information processing systems 33 (2020) 6840\u20136851."},{"key":"e_1_3_3_2_33_1","volume-title":"Mitsuba 3 renderer","author":"Jakob Wenzel","year":"2022","unstructured":"Wenzel Jakob, S\u00e9bastien Speierer, Nicolas Roussel, Merlin Nimier-David, Delio Vicini, Tizian Zeltner, Baptiste Nicolet, Miguel Crespo, Vincent Leroy, and Ziyi Zhang. 2022. Mitsuba 3 renderer. https:\/\/mitsuba-renderer.org."},{"key":"e_1_3_3_2_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"e_1_3_3_2_35_1","doi-asserted-by":"crossref","unstructured":"James\u00a0T Kajiya and Brian\u00a0P Von\u00a0Herzen. 1984. Ray tracing volume densities. ACM SIGGRAPH computer graphics 18 3 (1984) 165\u2013174.","DOI":"10.1145\/964965.808594"},{"key":"e_1_3_3_2_36_1","doi-asserted-by":"crossref","unstructured":"Isaac\u00a0E Lagaris Aristidis Likas and Dimitrios\u00a0I Fotiadis. 1998. Artificial neural networks for solving ordinary and partial differential equations. IEEE transactions on neural networks 9 5 (1998) 987\u20131000.","DOI":"10.1109\/72.712178"},{"key":"e_1_3_3_2_37_1","volume-title":"Thirty-seventh Conference on Neural Information Processing Systems","author":"Li Jinxi","year":"2023","unstructured":"Jinxi Li, Ziyang Song, and Bo Yang. 2023b. NVFi: Neural Velocity Fields for 3D Physics Learning from Dynamic Videos. In Thirty-seventh Conference on Neural Information Processing Systems."},{"key":"e_1_3_3_2_38_1","unstructured":"Xuan Li Yi-Ling Qiao Peter\u00a0Yichen Chen Krishna\u00a0Murthy Jatavallabhula Ming Lin Chenfanfu Jiang and Chuang Gan. 2023a. PAC-neRF: Physics augmented continuum neural radiance fields for geometry-agnostic system identification. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2303.05512 (2023)."},{"key":"e_1_3_3_2_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_8"},{"key":"e_1_3_3_2_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00643"},{"key":"e_1_3_3_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00416"},{"key":"e_1_3_3_2_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00010"},{"key":"e_1_3_3_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00821"},{"key":"e_1_3_3_2_44_1","unstructured":"Pingchuan Ma Peter\u00a0Yichen Chen Bolei Deng Joshua\u00a0B Tenenbaum Tao Du Chuang Gan and Wojciech Matusik. 2023. Learning Neural Constitutive Laws From Motion Observations for Generalizable PDE Dynamics. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2304.14369 (2023)."},{"key":"e_1_3_3_2_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"e_1_3_3_2_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01129"},{"key":"e_1_3_3_2_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58610-2_12"},{"key":"e_1_3_3_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00571"},{"key":"e_1_3_3_2_49_1","doi-asserted-by":"crossref","unstructured":"Makoto Okabe Yoshinori Dobashi Ken Anjyo and Rikio Onai. 2015. Fluid volume modeling from sparse multi-view images by appearance transfer. ACM Transactions on Graphics (TOG) 34 4 (2015) 1\u201310.","DOI":"10.1145\/2766958"},{"key":"e_1_3_3_2_50_1","doi-asserted-by":"crossref","unstructured":"Samira Pakravan Pouria\u00a0A Mistani Miguel\u00a0A Aragon-Calvo and Frederic Gibou. 2021. Solving inverse-PDE problems with physics-aware neural networks. J. Comput. Phys. 440 (2021) 110414.","DOI":"10.1016\/j.jcp.2021.110414"},{"key":"e_1_3_3_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00410"},{"key":"e_1_3_3_2_52_1","unstructured":"Adam Paszke Sam Gross Soumith Chintala Gregory Chanan Edward Yang Zachary DeVito Zeming Lin Alban Desmaison Luca Antiga and Adam Lerer. 2017. Automatic differentiation in pytorch. (2017)."},{"key":"e_1_3_3_2_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00060"},{"key":"e_1_3_3_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01018"},{"key":"e_1_3_3_2_55_1","unstructured":"Yi-Ling Qiao Alexander Gao and Ming Lin. 2022. Neuphysics: Editable neural geometry and physics from monocular videos. Advances in Neural Information Processing Systems 35 (2022) 12841\u201312854."},{"key":"e_1_3_3_2_56_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14270"},{"key":"e_1_3_3_2_57_1","doi-asserted-by":"crossref","unstructured":"Maziar Raissi Paris Perdikaris and George\u00a0E Karniadakis. 2019. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics 378 (2019) 686\u2013707.","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"e_1_3_3_2_58_1","doi-asserted-by":"crossref","unstructured":"Maziar Raissi Alireza Yazdani and George\u00a0Em Karniadakis. 2020. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations. Science 367 6481 (2020) 1026\u20131030.","DOI":"10.1126\/science.aaw4741"},{"key":"e_1_3_3_2_59_1","doi-asserted-by":"crossref","unstructured":"Bo Ren Chenfeng Li Xiao Yan Ming\u00a0C Lin Javier Bonet and Shi-Min Hu. 2014. Multiple-fluid SPH simulation using a mixture model. ACM Transactions on Graphics (TOG) 33 5 (2014) 1\u201311.","DOI":"10.1145\/2645703"},{"key":"e_1_3_3_2_60_1","unstructured":"Javier\u00a0E Santos Zachary\u00a0R Fox Arvind Mohan Daniel O\u2019Malley Hari Viswanathan and Nicholas Lubbers. 2023. Development of the Senseiver for efficient field reconstruction from sparse observations. Nature Machine Intelligence (2023) 1\u20139."},{"key":"e_1_3_3_2_61_1","doi-asserted-by":"crossref","unstructured":"Justin Sirignano and Konstantinos Spiliopoulos. 2018. DGM: A deep learning algorithm for solving partial differential equations. Journal of computational physics 375 (2018) 1339\u20131364.","DOI":"10.1016\/j.jcp.2018.08.029"},{"key":"e_1_3_3_2_62_1","unstructured":"Vincent Sitzmann Julien Martel Alexander Bergman David Lindell and Gordon Wetzstein. 2020. Implicit neural representations with periodic activation functions. Advances in neural information processing systems 33 (2020) 7462\u20137473."},{"key":"e_1_3_3_2_63_1","doi-asserted-by":"publisher","unstructured":"Liangchen Song Anpei Chen Zhong Li Zhang Chen Lele Chen Junsong Yuan Yi Xu and Andreas Geiger. 2023. NeRFPlayer: A Streamable Dynamic Scene Representation with Decomposed Neural Radiance Fields. IEEE Transactions on Visualization and Computer Graphics 29 5 (2023) 2732\u20132742. 10.1109\/TVCG.2023.3247082 https:\/\/dl.acm.org\/doi\/10.1109\/TVCG.2023.3247082","DOI":"10.1109\/TVCG.2023.3247082"},{"key":"e_1_3_3_2_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/311535.311548"},{"key":"e_1_3_3_2_65_1","unstructured":"Shih-Yang Su Frank Yu Michael Zollh\u00f6fer and Helge Rhodin. 2021. A-nerf: Articulated neural radiance fields for learning human shape appearance and pose. Advances in Neural Information Processing Systems 34 (2021) 12278\u201312291."},{"key":"e_1_3_3_2_66_1","unstructured":"Matthew Tancik Pratul Srinivasan Ben Mildenhall Sara Fridovich-Keil Nithin Raghavan Utkarsh Singhal Ravi Ramamoorthi Jonathan Barron and Ren Ng. 2020. Fourier features let networks learn high frequency functions in low dimensional domains. Advances in Neural Information Processing Systems 33 (2020) 7537\u20137547."},{"key":"e_1_3_3_2_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01272"},{"key":"e_1_3_3_2_68_1","unstructured":"Kiwon Um Robert Brand Yun\u00a0Raymond Fei Philipp Holl and Nils Thuerey. 2020. Solver-in-the-loop: Learning from differentiable physics to interact with iterative pde-solvers. Advances in Neural Information Processing Systems 33 (2020) 6111\u20136122."},{"key":"e_1_3_3_2_69_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_3_2_70_1","volume-title":"Thirty-seventh Conference on Neural Information Processing Systems","author":"Wang Feng","year":"2023","unstructured":"Feng Wang, Zilong Chen, Guokang Wang, Yafei Song, and Huaping Liu. 2023a. Masked Space-Time Hash Encoding for Efficient Dynamic Scene Reconstruction. In Thirty-seventh Conference on Neural Information Processing Systems."},{"key":"e_1_3_3_2_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01805"},{"key":"e_1_3_3_2_72_1","volume-title":"Thirty-seventh Conference on Neural Information Processing Systems","author":"Wang Tsun-Hsuan","year":"2023","unstructured":"Tsun-Hsuan Wang, Juntian Zheng, Pingchuan Ma, Yilun Du, Byungchul Kim, Andrew\u00a0Everett Spielberg, Joshua\u00a0B. Tenenbaum, Chuang Gan, and Daniela Rus. 2023c. DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models. In Thirty-seventh Conference on Neural Information Processing Systems. https:\/\/openreview.net\/forum?id=1zo4iioUEs"},{"key":"e_1_3_3_2_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01573"},{"key":"e_1_3_3_2_74_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00930"},{"key":"e_1_3_3_2_75_1","doi-asserted-by":"crossref","unstructured":"You Xie Erik Franz Mengyu Chu and Nils Thuerey. 2018. tempoGAN: A temporally coherent volumetric GAN for super-resolution fluid flow. ACM Transactions on Graphics (TOG) 37 4 (2018) 1\u201315.","DOI":"10.1145\/3197517.3201304"},{"key":"e_1_3_3_2_76_1","unstructured":"Hongyi Xu Thiemo Alldieck and Cristian Sminchisescu. 2021. H-nerf: Neural radiance fields for rendering and temporal reconstruction of humans in motion. Advances in Neural Information Processing Systems 34 (2021) 14955\u201314966."},{"key":"e_1_3_3_2_77_1","volume-title":"Thirty-seventh Conference on Neural Information Processing Systems","author":"Yu Hong-Xing","year":"2023","unstructured":"Hong-Xing Yu, Yang Zheng, Yuan Gao, Yitong Deng, Bo Zhu, and Jiajun Wu. 2023. Inferring Hybrid Neural Fluid Fields from Videos. In Thirty-seventh Conference on Neural Information Processing Systems."},{"key":"e_1_3_3_2_78_1","volume-title":"Thirty-seventh Conference on Neural Information Processing Systems","author":"Yue Zongsheng","year":"2023","unstructured":"Zongsheng Yue, Jianyi Wang, and Chen\u00a0Change Loy. 2023. ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting. In Thirty-seventh Conference on Neural Information Processing Systems."},{"key":"e_1_3_3_2_79_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00194"},{"key":"e_1_3_3_2_80_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"e_1_3_3_2_81_1","unstructured":"Yulun Zhang Kunpeng Li Kai Li Bineng Zhong and Yun Fu. 2019. Residual non-local attention networks for image restoration. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1903.10082 (2019)."}],"event":{"name":"SA '24: SIGGRAPH Asia 2024 Conference Papers","location":"Tokyo Japan","acronym":"SA '24","sponsor":["SIGGRAPH ACM Special Interest Group on Computer Graphics and Interactive Techniques"]},"container-title":["SIGGRAPH Asia 2024 Conference Papers"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3680528.3687667","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3680528.3687667","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:20Z","timestamp":1750295900000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3680528.3687667"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"references-count":80,"alternative-id":["10.1145\/3680528.3687667","10.1145\/3680528"],"URL":"https:\/\/doi.org\/10.1145\/3680528.3687667","relation":{},"subject":[],"published":{"date-parts":[[2024,12,3]]},"assertion":[{"value":"2024-12-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}