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Beyond artistic style transfer, the capability to perform fine-grained visual feature transfer is becoming increasingly important. Embroidery is a textile art form characterized by intricate interplay of diverse stitch patterns and material properties, which poses unique challenges for existing style transfer methods. To explore the customization for such fine-grained features, we propose a novel contrastive learning framework that disentangles fine-grained style and content features with a single reference image, building on the classic concept of image analogy. We first construct an image pair to define the target style, and then adopt a similarity metric based on the decoupled representations of pretrained diffusion models for style-content separation. Subsequently, we propose a two-stage contrastive LoRA modulation technique to capture fine-grained style features. In the first stage, we iteratively update the whole LoRA and the selected style blocks to initially separate style from content. In the second stage, we design a contrastive learning strategy to further decouple style and content through self-knowledge distillation. Finally, we build an inference pipeline to handle image or text inputs with only the style blocks. To evaluate our method on fine-grained style transfer, we build a benchmark for embroidery customization. Our approach surpasses prior methods on this task and further demonstrates strong generalization to three additional domains: artistic style transfer, sketch colorization, and appearance transfer. Our project is available at: https:\/\/style3d.github.io\/embroidery_customization.<\/jats:p>","DOI":"10.1145\/3763290","type":"journal-article","created":{"date-parts":[[2025,12,4]],"date-time":"2025-12-04T17:15:39Z","timestamp":1764868539000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["One-shot Embroidery Customization via Contrastive LoRA Modulation"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1228-7369","authenticated-orcid":false,"given":"Jun","family":"Ma","sequence":"first","affiliation":[{"name":"Zhejiang Sci-Tech University, Hangzhou, China"},{"name":"Style3D Research, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9728-7113","authenticated-orcid":false,"given":"Qian","family":"He","sequence":"additional","affiliation":[{"name":"State Key Lab of CAD and CG, Zhejiang University, Hangzhou, China"},{"name":"Style3D Research, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3505-8608","authenticated-orcid":false,"given":"Gaofeng","family":"He","sequence":"additional","affiliation":[{"name":"Style3D Research, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3111-9269","authenticated-orcid":false,"given":"Huang","family":"Chen","sequence":"additional","affiliation":[{"name":"Style3D Research, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7164-3770","authenticated-orcid":false,"given":"Chen","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Lab of CAD and CG, Zhejiang University, Hangzhou, China"},{"name":"Style3D Research, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7339-2920","authenticated-orcid":false,"given":"Xiaogang","family":"Jin","sequence":"additional","affiliation":[{"name":"State Key Lab of CAD and CG, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8153-2337","authenticated-orcid":false,"given":"Huamin","family":"Wang","sequence":"additional","affiliation":[{"name":"Style3D Research, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,12,4]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al.","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. 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