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However, 2D VTON methods struggle to convey depth and immersion, while existing 3D methods require multi\u2010view garment images and face challenges in generating high\u2010fidelity garment textures. To address the aforementioned limitations, this paper proposes a panoramic Gaussian VTON framework guided solely by front\u2010and\u2010back garment information, named PG\u2010VTON, which uses an adapted local controllable diffusion model for generating virtual dressing effects in specific regions. Specifically, PG\u2010VTON adopts a coarse\u2010to\u2010fine architecture consisting of two stages. The coarse editing stage employs a local controllable diffusion model with a score distillation sampling (SDS) loss to generate coarse garment geometries with high\u2010level semantics. Meanwhile, the refinement stage applies the same diffusion model with a photometric loss not only to enhance garment details and reduce artifacts but also to correct unwanted noise and distortions introduced during the coarse stage, thereby effectively enhancing realism. To improve training efficiency, we further introduce a dynamic noise scheduling (DNS) strategy, which ensures stable training and high\u2010fidelity results. Experimental results demonstrate the superiority of our method, which achieves geometrically consistent and highly realistic 3D virtual try\u2010on generation.<\/jats:p>","DOI":"10.1002\/cav.70054","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T01:55:49Z","timestamp":1748397349000},"update-policy":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["PG\u2010VTON: Front\u2010And\u2010Back Garment Guided Panoramic Gaussian Virtual Try\u2010On With Diffusion Modeling"],"prefix":"10.1002","volume":"36","author":[{"ORCID":"https:\/\/summer-heart-0930.chufeiyun1688.workers.dev:443\/https\/orcid.org\/0009-0001-8607-3926","authenticated-orcid":false,"given":"Jian","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology Hangzhou Dianzi University  Hangzhou Zhejiang China"}]},{"given":"Shengwei","family":"Sang","sequence":"additional","affiliation":[{"name":"Zhejiang Desman Intelligent Technology Co. 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Zhejiang China"}]},{"given":"Yifei","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Hangzhou Dianzi University  Hangzhou Zhejiang China"}]},{"given":"Guojun","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Hangzhou Dianzi University  Hangzhou Zhejiang China"}]},{"given":"Xiaoyang","family":"Mao","sequence":"additional","affiliation":[{"name":"Faculty of Engineering University of Yamanashi  Kofu Yamanashi Japan"}]},{"given":"Wenhui","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Hangzhou Dianzi University  Hangzhou Zhejiang China"}]}],"member":"311","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00359"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3531017"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612137"},{"key":"e_1_2_11_5_1","doi-asserted-by":"crossref","unstructured":"Y.Choi S.Kwak K.Lee H.Choi andJ.Shin \u201cImproving Diffusion Models for Virtual Try\u2010On \u201d arXiv preprint arXiv:2403.05139 (2024).","DOI":"10.1007\/978-3-031-73016-0_13"},{"key":"e_1_2_11_6_1","first-page":"12110","article-title":"ULNeF: Untangled Layered Neural Fields for Mix\u2010And\u2010Match Virtual Try\u2010On","volume":"35","author":"Santesteban I.","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_11_7_1","first-page":"11763","volume-title":"CVPR","author":"Santesteban I.","year":"2021"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503250"},{"key":"e_1_2_11_9_1","first-page":"19740","volume-title":"Proceedings of the ICCVIEEE","author":"Haque A.","year":"2023"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3592433"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02029"},{"key":"e_1_2_11_12_1","unstructured":"J.Tang J.Ren H.Zhou Z.Liu andG.Zeng \u201cDreamgaussian: Generative Gaussian Splatting for Efficient 3d Content Creation \u201d arXiv preprint arXiv:2309.16653 (2023)."},{"key":"e_1_2_11_13_1","unstructured":"B.Poole A.Jain J. 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