{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T06:05:49Z","timestamp":1784268349516,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62106235"],"award-info":[{"award-number":["62106235"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3681025","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:33Z","timestamp":1729925973000},"page":"476-485","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["LoopGaussian: Creating 3D Cinemagraph with Multi-view Images via Eulerian Motion Field"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8113-9799","authenticated-orcid":false,"given":"Jiyang","family":"Li","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7546-9052","authenticated-orcid":false,"given":"Lechao","family":"Cheng","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8218-6464","authenticated-orcid":false,"given":"Zhangye","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6315-3432","authenticated-orcid":false,"given":"Tingting","family":"Mu","sequence":"additional","affiliation":[{"name":"University of Manchester, Manchester, United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5858-5174","authenticated-orcid":false,"given":"Jingxuan","family":"He","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Computer Graphics Forum","author":"Bai Jiamin","unstructured":"Jiamin Bai, Aseem Agarwala, Maneesh Agrawala, and Ravi Ramamoorthi. 2013. Automatic cinemagraph portraits. In Computer Graphics Forum, Vol. 32. Wiley Online Library, 17--25."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00580"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00021"},{"key":"e_1_3_2_1_4_1","volume-title":"Segment any 3d gaussians. arXiv preprint arXiv:2312.00860","author":"Cen Jiazhong","year":"2023","unstructured":"Jiazhong Cen, Jiemin Fang, Chen Yang, Lingxi Xie, Xiaopeng Zhang, Wei Shen, and Qi Tian. 2023. Segment any 3d gaussians. arXiv preprint arXiv:2312.00860 (2023)."},{"key":"e_1_3_2_1_5_1","volume-title":"A Survey on 3D Gaussian Splatting. arXiv preprint arXiv:2401.03890","author":"Chen Guikun","year":"2024","unstructured":"Guikun Chen and Wenguan Wang. 2024. A Survey on 3D Gaussian Splatting. arXiv preprint arXiv:2401.03890 (2024)."},{"key":"e_1_3_2_1_6_1","volume-title":"StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN. arXiv preprint arXiv:2403.14186","author":"Choi Jongwoo","year":"2024","unstructured":"Jongwoo Choi, Kwanggyoon Seo, Amirsaman Ashtari, and Junyong Noh. 2024. StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN. arXiv preprint arXiv:2403.14186 (2024)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1186822.1073273"},{"key":"e_1_3_2_1_8_1","volume-title":"Neural parametric gaussians for monocular non-rigid object reconstruction. arXiv preprint arXiv:2312.01196","author":"Das Devikalyan","year":"2023","unstructured":"Devikalyan Das, Christopher Wewer, Raza Yunus, Eddy Ilg, and Jan Eric Lenssen. 2023. Neural parametric gaussians for monocular non-rigid object reconstruction. arXiv preprint arXiv:2312.01196 (2023)."},{"key":"e_1_3_2_1_9_1","volume-title":"4D Gaussian Splatting: Towards Efficient Novel View Synthesis for Dynamic Scenes. arXiv preprint arXiv:2402.03307","author":"Duan Yuanxing","year":"2024","unstructured":"Yuanxing Duan, Fangyin Wei, Qiyu Dai, Yuhang He, Wenzheng Chen, and Baoquan Chen. 2024. 4D Gaussian Splatting: Towards Efficient Novel View Synthesis for Dynamic Scenes. arXiv preprint arXiv:2402.03307 (2024)."},{"key":"e_1_3_2_1_10_1","volume-title":"Cinemagraphs: What it looks like when a photo moves. The Washington Post","author":"Flock Elisabeth","year":"2011","unstructured":"Elisabeth Flock. 2011. Cinemagraphs: What it looks like when a photo moves. The Washington Post (2011)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01201"},{"key":"e_1_3_2_1_12_1","volume-title":"Benchmarking Micro-action Recognition: Dataset, Method, and Application","author":"Guo Dan","year":"2024","unstructured":"Dan Guo, Kun Li, Bin Hu, Yan Zhang, and Meng Wang. 2024. Benchmarking Micro-action Recognition: Dataset, Method, and Application. IEEE Transactions on Circuits and Systems for Video Technology (2024)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3450626.3459935"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00582"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00575"},{"key":"e_1_3_2_1_16_1","volume-title":"SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes. arXiv preprint arXiv:2312.14937","author":"Huang Yi-Hua","year":"2023","unstructured":"Yi-Hua Huang, Yang-Tian Sun, Ziyi Yang, Xiaoyang Lyu, Yan-Pei Cao, and Xiaojuan Qi. 2023. SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes. arXiv preprint arXiv:2312.14937 (2023)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3592433"},{"key":"e_1_3_2_1_19_1","volume-title":"GP-NeRF: Generalized Perception NeRF for Context-Aware 3D Scene Understanding. arXiv preprint arXiv:2311.11863","author":"Li Hao","year":"2023","unstructured":"Hao Li, Dingwen Zhang, Yalun Dai, Nian Liu, Lechao Cheng, Jingfeng Li, Jingdong Wang, and Junwei Han. 2023. GP-NeRF: Generalized Perception NeRF for Context-Aware 3D Scene Understanding. arXiv preprint arXiv:2311.11863 (2023)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00446"},{"key":"e_1_3_2_1_21_1","volume-title":"Spacetime gaussian feature splatting for real-time dynamic view synthesis. arXiv preprint arXiv:2312.16812","author":"Li Zhan","year":"2023","unstructured":"Zhan Li, Zhang Chen, Zhong Li, and Yi Xu. 2023. Spacetime gaussian feature splatting for real-time dynamic view synthesis. arXiv preprint arXiv:2312.16812 (2023)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2816795.2818061","article-title":"Fast computation of seamless video loops","volume":"34","author":"Liao Jing","year":"2015","unstructured":"Jing Liao, Mark Finch, and Hugues Hoppe. 2015. Fast computation of seamless video loops. ACM Transactions on Graphics (TOG), Vol. 34, 6 (2015), 1--10.","journal-title":"ACM Transactions on Graphics (TOG)"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-018-6332-7"},{"key":"e_1_3_2_1_24_1","volume-title":"Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle. arXiv:2312.03431","author":"Lin Youtian","year":"2023","unstructured":"Youtian Lin, Zuozhuo Dai, Siyu Zhu, and Yao Yao. 2023. Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle. arXiv:2312.03431 (2023)."},{"key":"e_1_3_2_1_25_1","volume-title":"Toward better boundary preserved supervoxel segmentation for 3D point clouds. ISPRS journal of photogrammetry and remote sensing","author":"Lin Yangbin","year":"2018","unstructured":"Yangbin Lin, Cheng Wang, Dawei Zhai, Wei Li, and Jonathan Li. 2018. Toward better boundary preserved supervoxel segmentation for 3D point clouds. ISPRS journal of photogrammetry and remote sensing, Vol. 143 (2018), 39--47."},{"key":"e_1_3_2_1_26_1","volume-title":"Antonio Torralba, Sanja Fidler, and Karsten Kreis.","author":"Ling Huan","year":"2023","unstructured":"Huan Ling, Seung Wook Kim, Antonio Torralba, Sanja Fidler, and Karsten Kreis. 2023. Align your gaussians: Text-to-4d with dynamic 3d gaussians and composed diffusion models. arXiv preprint arXiv:2312.13763 (2023)."},{"key":"e_1_3_2_1_27_1","first-page":"15651","article-title":"Neural sparse voxel fields","volume":"33","author":"Liu Lingjie","year":"2020","unstructured":"Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. 2020. Neural sparse voxel fields. Advances in Neural Information Processing Systems, Vol. 33 (2020), 15651--15663.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Jonathon Luiten Georgios Kopanas Bastian Leibe and Deva Ramanan. 2024. Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis. In 3DV.","DOI":"10.1109\/3DV62453.2024.00044"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00038"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00365"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3618326"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503250"},{"key":"e_1_3_2_1_33_1","volume-title":"Instant neural graphics primitives with a multiresolution hash encoding. ACM transactions on graphics (TOG)","author":"M\u00fcller Thomas","year":"2022","unstructured":"Thomas M\u00fcller, Alex Evans, Christoph Schied, and Alexander Keller. 2022. Instant neural graphics primitives with a multiresolution hash encoding. ACM transactions on graphics (TOG), Vol. 41, 4 (2022), 1--15."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1080\/02693799008941549"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.264"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00581"},{"key":"e_1_3_2_1_37_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01018"},{"key":"e_1_3_2_1_39_1","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition. 652--660","author":"Qi Charles R","year":"2017","unstructured":"Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. 2017. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 652--660."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01407"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01976"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3596711.3596769"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/280814.280882"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVMP.2011.16"},{"key":"e_1_3_2_1_45_1","volume-title":"FVD: A new metric for video generation.","author":"Unterthiner Thomas","year":"2019","unstructured":"Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Rapha\u00ebl Marinier, Marcin Michalski, and Sylvain Gelly. 2019. FVD: A new metric for video generation. (2019)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00541"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01661"},{"key":"e_1_3_2_1_48_1","volume-title":"4d gaussian splatting for real-time dynamic scene rendering. arXiv preprint arXiv:2310.08528","author":"Wu Guanjun","year":"2023","unstructured":"Guanjun Wu, Taoran Yi, Jiemin Fang, Lingxi Xie, Xiaopeng Zhang, Wei Wei, Wenyu Liu, Qi Tian, and Xinggang Wang. 2023. 4d gaussian splatting for real-time dynamic scene rendering. arXiv preprint arXiv:2310.08528 (2023)."},{"key":"e_1_3_2_1_49_1","volume-title":"Physgaussian: Physics-integrated 3d gaussians for generative dynamics. arXiv preprint arXiv:2311.12198","author":"Xie Tianyi","year":"2023","unstructured":"Tianyi Xie, Zeshun Zong, Yuxin Qiu, Xuan Li, Yutao Feng, Yin Yang, and Chenfanfu Jiang. 2023. Physgaussian: Physics-integrated 3d gaussians for generative dynamics. arXiv preprint arXiv:2311.12198 (2023)."},{"key":"e_1_3_2_1_50_1","volume-title":"Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction. arXiv preprint arXiv:2309.13101","author":"Yang Ziyi","year":"2023","unstructured":"Ziyi Yang, Xinyu Gao, Wen Zhou, Shaohui Jiao, Yuqing Zhang, and Xiaogang Jin. 2023. Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction. arXiv preprint arXiv:2309.13101 (2023)."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/2393347.2396406"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00570"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"e_1_3_2_1_54_1","volume-title":"Stereo magnification: Learning view synthesis using multiplane images. arXiv preprint arXiv:1805.09817","author":"Zhou Tinghui","year":"2018","unstructured":"Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. 2018. Stereo magnification: Learning view synthesis using multiplane images. arXiv preprint arXiv:1805.09817 (2018)."}],"event":{"name":"MM '24: The 32nd ACM International Conference on Multimedia","location":"Melbourne VIC Australia","acronym":"MM '24","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 32nd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3681025","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3681025","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:37Z","timestamp":1750295857000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3681025"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":54,"alternative-id":["10.1145\/3664647.3681025","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3681025","relation":{},"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"2024-10-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}