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A single forward pass yields a set of 3D Gaussian primitives encoding both scene geometry and appearance, and the corresponding camera intrinsics and extrinsics for each input image. This unified design scales effortlessly to casually captured, multi-view datasets without any pose annotations. In extensive zero-shot evaluations, AnySplat matches the quality of pose-aware baselines in both sparse- and dense-view scenarios while surpassing existing pose-free approaches. Moreover, it greatly reduces rendering latency compared to optimization-based neural fields, bringing real-time novel-view synthesis within reach for unconstrained capture settings. Project page: https:\/\/city-super.github.io\/anysplat\/.<\/jats:p>","DOI":"10.1145\/3763326","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T17:15:39Z","timestamp":1764868539000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2899-273X","authenticated-orcid":false,"given":"Lihan","family":"Jiang","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"},{"name":"Shanghai Artificial Intelligence Laboratory, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7124-4431","authenticated-orcid":false,"given":"Yucheng","family":"Mao","sequence":"additional","affiliation":[{"name":"Shanghai Artificial Intelligence Laboratory, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1026-2410","authenticated-orcid":false,"given":"Linning","family":"Xu","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8830-3820","authenticated-orcid":false,"given":"Tao","family":"Lu","sequence":"additional","affiliation":[{"name":"Brown University, Rhode Island, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8010-5733","authenticated-orcid":false,"given":"Kerui","family":"Ren","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai, China"},{"name":"Shanghai Artificial Intelligence Laboratory, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6213-2038","authenticated-orcid":false,"given":"Yichen","family":"Jin","sequence":"additional","affiliation":[{"name":"Shanghai Artificial Intelligence Laboratory, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8858-0918","authenticated-orcid":false,"given":"Xudong","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Artificial Intelligence Laboratory, Shanghai, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0327-4547","authenticated-orcid":false,"given":"Mulin","family":"Yu","sequence":"additional","affiliation":[{"name":"Shanghai Artificial Intelligence Laboratory, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6711-9319","authenticated-orcid":false,"given":"Jiangmiao","family":"Pang","sequence":"additional","affiliation":[{"name":"Shanghai Artificial Intelligence Laboratory, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6767-8105","authenticated-orcid":false,"given":"Feng","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8865-7896","authenticated-orcid":false,"given":"Dahua","family":"Lin","sequence":"additional","affiliation":[{"name":"Chinese University of Hong Kong, Hong Kong, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0777-9232","authenticated-orcid":false,"given":"Bo","family":"Dai","sequence":"additional","affiliation":[{"name":"University of Hong Kong, Hong Kong, China"}]}],"member":"320","published-online":{"date-parts":[[2025,12,4]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00539"},{"key":"e_1_2_2_2_1","volume-title":"Arkitscenes: A diverse real-world dataset for 3d indoor scene understanding using mobile rgb-d data. arXiv preprint arXiv:2111.08897","author":"Baruch Gilad","year":"2021","unstructured":"Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Tal Dimry, Yuri Feigin, Peter Fu, Thomas Gebauer, Brandon Joffe, Daniel Kurz, Arik Schwartz, et al. 2021. 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