[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-lzhbrian--Cool-Fashion-Papers":3,"tool-lzhbrian--Cool-Fashion-Papers":64},[4,17,27,35,43,56],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":16},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,3,"2026-04-05T11:01:52",[13,14,15],"开发框架","图像","Agent","ready",{"id":18,"name":19,"github_repo":20,"description_zh":21,"stars":22,"difficulty_score":23,"last_commit_at":24,"category_tags":25,"status":16},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",140436,2,"2026-04-05T23:32:43",[13,15,26],"语言模型",{"id":28,"name":29,"github_repo":30,"description_zh":31,"stars":32,"difficulty_score":23,"last_commit_at":33,"category_tags":34,"status":16},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107662,"2026-04-03T11:11:01",[13,14,15],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":23,"last_commit_at":41,"category_tags":42,"status":16},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[13,26],{"id":44,"name":45,"github_repo":46,"description_zh":47,"stars":48,"difficulty_score":23,"last_commit_at":49,"category_tags":50,"status":16},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[14,51,52,53,15,54,26,13,55],"数据工具","视频","插件","其他","音频",{"id":57,"name":58,"github_repo":59,"description_zh":60,"stars":61,"difficulty_score":10,"last_commit_at":62,"category_tags":63,"status":16},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[15,14,13,26,54],{"id":65,"github_repo":66,"name":67,"description_en":68,"description_zh":69,"ai_summary_zh":69,"readme_en":70,"readme_zh":71,"quickstart_zh":72,"use_case_zh":73,"hero_image_url":74,"owner_login":75,"owner_name":76,"owner_avatar_url":77,"owner_bio":78,"owner_company":79,"owner_location":79,"owner_email":80,"owner_twitter":79,"owner_website":78,"owner_url":81,"languages":82,"stars":91,"forks":92,"last_commit_at":93,"license":94,"difficulty_score":95,"env_os":96,"env_gpu":97,"env_ram":97,"env_deps":98,"category_tags":101,"github_topics":102,"view_count":23,"oss_zip_url":79,"oss_zip_packed_at":79,"status":16,"created_at":115,"updated_at":116,"faqs":117,"releases":118},4077,"lzhbrian\u002FCool-Fashion-Papers","Cool-Fashion-Papers","👔👗🕶️🎩 Cool resources about Fashion + AI! (papers, datasets, workshops, companies, ...) (constantly updating)","Cool-Fashion-Papers 是一个专注于时尚与人工智能交叉领域的开源资源库，旨在汇集该方向最前沿的学术论文、数据集、行业会议及相关企业信息。它主要解决了时尚科技领域研究资料分散、难以系统性获取的痛点，为从业者提供了一个持续更新的一站式知识索引。\n\n该资源库特别适合 AI 研究人员、计算机视觉开发者以及时尚科技公司的技术团队使用。无论是希望了解虚拟试衣最新算法的研究者，还是寻找训练数据的产品经理，都能在此快速定位所需内容。其核心亮点在于对文献进行了细致的分类整理，涵盖图像合成（如高保真虚拟试衣）、服装分类、个性化推荐及潮流预测等关键方向。列表中收录了包括 VITON-HD、CIT 在内的多个经典模型，并详细标注了论文出处、arXiv 编号及对应的代码项目链接。通过按时间顺序梳理技术演进路径，Cool-Fashion-Papers 不仅帮助用户高效追踪学术动态，也为复现先进算法和开展创新应用提供了坚实的资源基础。","# Cool Fashion Papers 👔👗🕶️🎩\n__Cool Fashion Related Papers and Resources (companies, datasets, conference, workshops, ...).__\n\nPapers are ordered in arXiv first version submitting time (if applicable).\n\nFeel free to send a PR or issue.\n\n\n__TOC__\n* [Papers](#papers)\n    * [Synthesis](#synthesis)\n    * [Classification](#classification)\n    * [Recommendation](#recommendation)\n    * [Forecast](#forecast)\n* [Related Events](#related-events)\n* [Datasets](#datasets)\n* [Companies](#companies)\n* [Other Useful Resources](#other-useful-resources)\n\n\n\n\n## Papers\n### Synthesis\n| Model | Title | Publication | Paper | Link |\n| ----- | ----- | ----------- | ----- | ---- |\n| Pose with style | Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN | SIGGRAPH ASIA 2021 | [[2109.06166]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.06166) | [[project]](https:\u002F\u002Fpose-with-style.github.io\u002F) |\n| CIT | CIT: Cloth Interactive Transformer for Virtual Try-On | arXiv | [[2104.05519]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.05519) | [[Amazingren \u002F CIT]](https:\u002F\u002Fgithub.com\u002FAmazingren\u002FCIT) |\n| VITON-HD | VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization | CVPR 2021 | [[2103.16874]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.16874) | [[shadow2496 \u002F VITON-HD]](https:\u002F\u002Fgithub.com\u002Fshadow2496\u002FVITON-HD) |\n| DCTON | Disentangled Cycle Consistency for Highly-realistic Virtual Try-On | CVPR 2021 | [[2103.09479]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.09479) | [[ChongjianGE \u002F DCTON]](https:\u002F\u002Fgithub.com\u002FChongjianGE\u002FDCTON) |\n| PF-AFN | Parser-Free Virtual Try-on via Distilling Appearance Flows | CVPR 2021 | [[2103.04559]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.04559) | [[geyuying \u002F PF-AFN]](https:\u002F\u002Fgithub.com\u002Fgeyuying\u002FPF-AFN) |\n| SieveNet | SieveNet: A Unified Framework for Robust Image-based Virtual Try-On | WACV 2020 | [[2001.06265]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.06265) |  |\n|  | Down to the Last Detail: Virtual Try-on with Detail Carving | arXiv | [[1912.06324]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.06324) | [[AIprogrammer \u002F Down-to-the-Last-Detail-Virtual-Try-on-with-Detail-Carving]](https:\u002F\u002Fgithub.com\u002FAIprogrammer\u002FDown-to-the-Last-Detail-Virtual-Try-on-with-Detail-Carving) |\n| ClothFlow | ClothFlow: A Flow-Based Model for Clothed Person Generation | ICCV 2019 | [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FHan_ClothFlow_A_Flow-Based_Model_for_Clothed_Person_Generation_ICCV_2019_paper.pdf) |  |\n| FW-GAN | FW-GAN: Flow-navigated Warping GAN for Video Virtual Try-on | ICCV 2019 | [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FDong_FW-GAN_Flow-Navigated_Warping_GAN_for_Video_Virtual_Try-On_ICCV_2019_paper.pdf) |  |\n|  | Virtually Trying on New Clothing with Arbitrary Poses | MM 2019 | [[paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3350946) | [[project]](https:\u002F\u002Ffashiontryon.wixsite.com\u002Ffashiontryon) |\n|  | Generating High-Resolution Fashion Model Images Wearing Custom Outfits | ICCVW 2019 | [[1908.08847]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.08847) |  |\n| Fashion++ | Fashion++: Minimal Edits for Outfit Improvement | ICCV 2019 | [[1904.09261]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.09261) | [[project]](http:\u002F\u002Fvision.cs.utexas.edu\u002Fprojects\u002FFashionPlus\u002F) |\n| MG-VTON | Towards Multi-pose Guided Virtual Try-on Network | arXiv | [[1902.11026]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.11026) |  |\n| FiNet | Compatible and Diverse Fashion Image Inpainting | ICCV 2019 | [[1902.01096]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.01096) |  |\n| M2E-Try On Net | M2E-Try On Net: Fashion from Model to Everyone | arXiv | [[1811.08599]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.08599) |  |\n| FashionGAN | FashionGAN: Display your fashion design using Conditional Generative Adversarial Nets | CG Forum 2018 | [[paper]](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fabs\u002F10.1111\u002Fcgf.13552) |  |\n| PIVTONS | PIVTONS: Pose Invariant Virtual Try-On Shoe with Conditional Image Completion | ACCV 2018 | [[paper]](https:\u002F\u002Fwinstonhsu.info\u002Fwp-content\u002Fuploads\u002F2018\u002F09\u002Fchou18PIVTONS.pdf) | [[project]](https:\u002F\u002Fwinstonhsu.info\u002Fpubs\u002Fpivtons-virtual-try-on-shoe\u002F) |\n| SwapNet | SwapNet: Image Based Garment Transfer | ECCV 2018 | [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ECCV_2018\u002Fpapers\u002FAmit_Raj_SwapNet_Garment_Transfer_ECCV_2018_paper.pdf) | [[andrewjong \u002F SwapNet]](https:\u002F\u002Fgithub.com\u002Fandrewjong\u002FSwapNet) |\n| FiLMedGAN | Language Guided Fashion Image Manipulation with Feature-wise Transformations | ECCVW 2018 | [[1808.04000]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.04000) |  |\n| CP-VITON | Toward Characteristic-Preserving Image-based Virtual Try-On Network | ECCV 2018 | [[1807.07688]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.07688) | [[sergeywong \u002F cp-vton]](https:\u002F\u002Fgithub.com\u002Fsergeywong\u002Fcp-vton) |\n|  | Disentangling Multiple Conditional Inputs in GANs | ECCVW 2018 | [[1806.07819]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07819) | [[zalandoresearch \u002F disentangling_conditional_gans]](https:\u002F\u002Fgithub.com\u002Fzalandoresearch\u002Fdisentangling_conditional_gans) |\n| DesIGN | DesIGN: Design Inspiration from Generative Networks | ECCVW 2018 | [[1804.00921]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00921) |  |\n| VITON | VITON: An Image-based Virtual Try-on Network | CVPR 2018 | [[1711.08447]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.08447) | [[xthan \u002F VITON]](https:\u002F\u002Fgithub.com\u002Fxthan\u002FVITON) |\n| DVBPR | Visually-Aware Fashion Recommendation and Design with Generative Image Models | ICDM 2017 | [[1711.02231]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.02231) | [[kang205 \u002F DVBPR]](https:\u002F\u002Fgithub.com\u002Fkang205\u002FDVBPR) |\n| FashionGAN | Be Your Own Prada: Fashion Synthesis with Structral Coherence. | ICCV 2017 | [[1710.07346]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.07346) | [[project]](http:\u002F\u002Fmmlab.ie.cuhk.edu.hk\u002Fprojects\u002FFashionGAN\u002F) |\n| CAGAN | The Conditional Analogy GAN: Swapping Fashion Articles on People Images | ICCVW 2017 | [[1709.04695]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.04695) |  |\n\n\n\n### Classification\n| Model | Title | Publication | Paper | Link |\n| ----- | ----- | ----------- | ----- | ---- |\n| DeepFashion2 | DeepFashion2: A Versatile Benchmark for Detection Pose Estimation Segmentation and Re-Identification of Clothing Images | CVPR 2019 | [[1901.07973]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.07973) | [[switchablenorms \u002F DeepFashion2]](https:\u002F\u002Fgithub.com\u002Fswitchablenorms\u002FDeepFashion2) |\n|  | Brand > Logo: Visual Analysis of Fashion Brands | ECCVW 2018 | [[1810.09941]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.09941) |  |\n| BCRNN | Attentive Fashion Grammar Network for Fashion Landmark Detection and Clothing Category Classification | CVPR 2018 | [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fpapers\u002FWang_Attentive_Fashion_Grammar_CVPR_2018_paper.pdf) |  |\n|  | Studio2Shop: from studio photo shoots to fashion articles | ICPRAM 2018 | [[1807.00556]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.00556) |  |\n| FashionBrain | FashionBrain Project: A Vision for Understanding Europe's Fashion Data Universe | KDDW 2017 | [[1710.09788]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.09788) | [[project]](https:\u002F\u002Ffashionbrain-project.eu\u002F) |\n|  | Automatic Spatially-aware Fashion Concept Discovery | ICCV 2017 | [[1708.01311]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.01311) | [[xthan \u002F fashion-200k]](https:\u002F\u002Fgithub.com\u002Fxthan\u002Ffashion-200k) |\n| DFA | Fashion Landmark Detection in the Wild | ECCV 2016 | [[1608.03049]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1608.03049) | [[liuziwei7 \u002F fashion-landmarks]](https:\u002F\u002Fgithub.com\u002Fliuziwei7\u002Ffashion-landmarks) |\n| FashionNet | DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations | CVPR 2016 | [[paper]](http:\u002F\u002Fwww.cv-foundation.org\u002Fopenaccess\u002Fcontent_cvpr_2016\u002Fpapers\u002FLiu_DeepFashion_Powering_Robust_CVPR_2016_paper.pdf) | [[project]](https:\u002F\u002Fliuziwei7.github.io\u002Fprojects\u002FDeepFashion.html) |\n\n\n\n### Recommendation\n| Model | Title | Publication | Paper | Link |\n| ----- | ----- | ----------- | ----- | ---- |\n|  | Semi-Supervised Visual Representation Learning for Fashion Compatibility | RecSys 2021 | [[2109.08052]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.08052) |  |\n| POG | POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion | KDD 2019 | [[1905.01866]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.01866) |  |\n|  | Aesthetic-based Clothing Recommendation | WWW 2018 | [[1809.05822]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.05822) |  |\n| CRAFT | CRAFT: Complementary Recommendations Using Adversarial Feature Transformer | ECCVW 2018 | [[1804.10871]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.10871) |  |\n|  | Learning Type-Aware Embeddings for Fashion Compatibility | ECCV 2018 | [[1803.09196]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.09196) |  |\n| NeuroStylist | NeuroStylist: Neural Compatibility Modeling for Clothing Matching | MM 2017 | [[paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3123314) |  |\n|  | Deep Cross-Domain Fashion Recommendation | RecSys 2017 | [[paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3109861) |  |\n|  | An LSTM-Based Dynamic Customer Model for Fashion Recommendation | RecSys 2017 | [[1708.07347]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.07347) |  |\n|  | Learning Fashion Compatibility with Bidirectional LSTMs | MM 2017 | [[1707.05691]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.05691) | [[xthan \u002F polyvore]](https:\u002F\u002Fgithub.com\u002Fxthan\u002Fpolyvore) |\n|  | Fashion DNA: Merging Content and Sales Data for Recommendation and Article Mapping | KDD 2016 | [[1609.02489]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.02489) |  |\n\n\n\n### Forecast\n| Model | Title | Publication | Paper | Link |\n| ----- | ----- | ----------- | ----- | ---- |\n| Style Quotient | Understanding Fashionability: What drives sales of a style? | KDDW 2018 | [[1806.11424]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.11424) |  |\n| Sales Potential | Sales Potential: Modelling Sellability of Visual Aesthetics of a Fashion Product | KDDW 2017 | [[paper]](https:\u002F\u002Fkddfashion2017.mybluemix.net\u002Ffinal_submissions\u002FML4Fashion_paper_10.pdf) |  |\n|  | Fashion Forward: Forecasting Visual Style in Fashion | ICCV 2017 | [[1705.06394]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.06394) |  |\n\n\n\n\n\n\n## Related Events\n1. KDD workshop on fashion [[2019]](https:\u002F\u002Fkddfashion2019.mybluemix.net\u002F) [[2018]](https:\u002F\u002Fkddfashion2018.mybluemix.net\u002F) [[2017]](https:\u002F\u002Fkddfashion2017.mybluemix.net\u002F) [[2016]](http:\u002F\u002Fkddfashion2016.mybluemix.net\u002F)\n2. Workshop on Computer Vision for Fashion, Art and Design [[CVPR 2020]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fcvcreative2020) [[ICCV 2019]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fcvcreative\u002Fhome) [[ECCV 2018]](https:\u002F\u002Fsites.google.com\u002Fview\u002Feccvfashion\u002F) [[ICCV 2017]](https:\u002F\u002Fsites.google.com\u002Fzalando.de\u002Fcvf-iccv2017\u002Fhome?authuser=0)\n3. NeurlPS workshop on Machine Learning for Creativity and Design [[2019]](https:\u002F\u002Fneurips2019creativity.github.io\u002F) [[2018]](https:\u002F\u002Fnips2018creativity.github.io\u002F) [[2017]](https:\u002F\u002Fnips2017creativity.github.io\u002F)\n4. SIGIR Workshop On eCommerce [[2019]](https:\u002F\u002Fsigir-ecom.github.io\u002Findex.html) [[2018]](https:\u002F\u002Fsigir-ecom.github.io\u002Fecom2018\u002Findex.html) [[2017]](http:\u002F\u002Fsigir-ecom.weebly.com\u002F) \n5. CVPR Deep Learning for Content Creation Tutorial [[2019]](https:\u002F\u002Fnvlabs.github.io\u002Fdl-for-content-creation\u002F)\n6. iMaterialist Fashion Challenge [[CVPR 2019]](https:\u002F\u002Fsites.google.com\u002Fview\u002Ffgvc6\u002Fcompetitions\u002Fimat-fashion-2019)\n7. iDesigner Challenge [[CVPR 2019]](https:\u002F\u002Fsites.google.com\u002Fview\u002Ffgvc6\u002Fcompetitions\u002Fidesigner-2019)\n8. FashionGen Challenge [[ICCV 2019, ECCV 2018]](https:\u002F\u002Ffashion-gen.com\u002F)\n9. JD AI Fashion Challenge [[ChinaMM 2018]](https:\u002F\u002Ffashion-challenge.github.io\u002F)\n10. Alibaba FashionAI Global Challenge [[Tianchi]](http:\u002F\u002Ffashionai.alibaba.com\u002F)\n11. Artificial Intelligence on Fashion and Textile Conference [[AIFT 2018]](https:\u002F\u002Fwww.polyu.edu.hk\u002Fitc\u002Faift2018\u002F)\n12. Fashion IQ Challenge [[CVPR 2020]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fcvcreative2020\u002Ffashion-iq?authuser=0) [[ICCV 2019]](https:\u002F\u002Fsites.google.com\u002Fview\u002Flingir\u002Ffashion-iq)\n13. DeepFashion2 Challenge [[CVPR 2020]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fcvcreative2020\u002Fdeepfashion2?authuser=0) [[ICCV 2019]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fcvcreative\u002Fdeepfashion2)\n\n\n\n\n## Datasets\n1. Fashionpedia [[website]](https:\u002F\u002Ffashionpedia.github.io\u002Fhome\u002Findex.html)\n2. DeepFashion2 Dataset [[website]](\u003Chttps:\u002F\u002Fgithub.com\u002Fswitchablenorms\u002FDeepFashion2>)\n3. DeepFashion Dataset [[website]](http:\u002F\u002Fmmlab.ie.cuhk.edu.hk\u002Fprojects\u002FDeepFashion.html)\n4. FashionGen [[website]](https:\u002F\u002Ffashion-gen.com\u002F)\n5. FashionAI [[Tianchi]](http:\u002F\u002Ffashionai.alibaba.com\u002Fdatasets\u002F?spm=a2c22.11190735.991137.8.501b6d83ilPJsX)\n6. TaobaoClothMatch [[Tianchi]](TaobaoClothMatch)\n7. Fashion-MNIST [[zalandoresearch\u002Ffashion-mnist]](https:\u002F\u002Fgithub.com\u002Fzalandoresearch\u002Ffashion-mnist)\n8. Fashion IQ [[website]](https:\u002F\u002Fwww.spacewu.com\u002Fposts\u002Ffashion-iq\u002F)\n\n\n\n## Companies\n\n| Brand                                        | Name                                                         | Found | Info                                        | News                                                         |\n| -------------------------------------------- | ------------------------------------------------------------ | ----- | ------------------------------------------- | ------------------------------------------------------------ |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_8b18aee6f357.png\" height=\"80px\">     | [Myntra](https:\u002F\u002Fwww.myntra.com\u002F)                            | 2007  | Forecast, Synthesis                         | [[2017.11 livemint]](https:\u002F\u002Fwww.livemint.com\u002F)              |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_4b6a95e4ed90.png\" height=\"50px\">    | [Alibaba 图像和美](https:\u002F\u002Fwww.leiphone.com\u002FaiWeights\u002Flab\u002F79) | 2009  | Recognition                                 | [[2018.7 FashionAI]](https:\u002F\u002Fwww.leiphone.com\u002Fnews\u002F201807\u002FBp6UlbXIab29rIN6.html) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_ea0d98c5baf1.png\" width=\"200px\">  | [STITCH FIX](https:\u002F\u002Fwww.stitchfix.com\u002F), [BLOG](https:\u002F\u002Fmultithreaded.stitchfix.com\u002Fblog\u002F) | 2011  | Personalization                             | [[2018.5 Forbes]](https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fbernardmarr\u002F2018\u002F05\u002F25\u002Fstitch-fix-the-amazing-use-case-of-using-artificial-intelligence-in-fashion-retail\u002F#5d5ff05c3292) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_773060603735.png\" width=\"200px\">  | [Heuritech](https:\u002F\u002Fwww3.heuritech.com\u002F)                     | 2013  | Forecast, Recognition                       | [[2019.1 Fashnerd]](https:\u002F\u002Ffashnerd.com\u002F2019\u002F01\u002Ffrench-startup-heuritech-wants-to-help-fashion-brands-make-clothes-that-customers-want\u002F) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_14ab29b07731.png\" height=\"50px\">      | [Yi+](http:\u002F\u002Fwww.dressplus.cn\u002Fhome)                          | 2014  | Recognition                                 | [[2018.8 funding]](https:\u002F\u002Fpe.pedaily.cn\u002F201808\u002F434505.shtml) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_8873772e0ea0.png\" height=\"80px\">     | [MALONG TECHNOLOGIES](http:\u002F\u002Fwww.malong.com\u002Fen\u002Fhome)         | 2014  | Recognition                                 | [[2018.7 Forbes]](https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fbernardmarr\u002F2018\u002F07\u002F09\u002F14-incredible-artificial-intelligence-pioneers-everyone-should-know-about\u002F#7a23aaaa5626) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_60b4df319c62.png\" height=\"80px\">       | [syte](https:\u002F\u002Fwww.syte.ai\u002F)                                 | 2015  | Recognition                                 | [[2018.12 co-op w\u002F farfetch]](https:\u002F\u002Ftechstartups.com\u002F2018\u002F12\u002F07\u002Fvisual-ai-startup-syte-partners-luxury-fashion-platform-farfetch-launch-new-app-visual-search-feature-ios\u002F) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_18255f6703cd.png\" width=\"200px\">  | [GrokStyle](https:\u002F\u002Fwww.grokstyle.com\u002F) (2019.2 acquired by [Facebook](https:\u002F\u002Fwww.facebook.com\u002F)) | 2015  | Searching                                   | [[2019.2 acquired by Facebook]](https:\u002F\u002Fventurebeat.com\u002F2019\u002F02\u002F08\u002Ffacebook-acquires-visual-search-startup-grokstyle\u002F) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_8fbbd443d71b.png\" width=\"200px\">    | [Zalando Research](https:\u002F\u002Fresearch.zalando.com\u002F)            | 2016  | Research                                    | [[2016.10 founding]](https:\u002F\u002Fearlymoves.com\u002F2016\u002F10\u002F07\u002Fzalando-research-is-shaping-the-future-of-online-fashion\u002F) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_99c15508f3c2.png\" width=\"200px\">     | [MatchU 码尚](https:\u002F\u002Fwww.immatchu.com\u002F)                     | 2016  | Modeling                                    | [[2018.12 funding]](http:\u002F\u002Fwww.iheima.com\u002Farticle-195955.html) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_f4e6801b799a.png\" width=\"200px\">     | [mode.ai](https:\u002F\u002Fmode.ai\u002F#\u002Fabout)                           | 2016  | Recognition, NLP, Searching                 | [[2018.5 TechRepublic]](https:\u002F\u002Fwww.techrepublic.com\u002Farticle\u002Fhow-mode-ai-powers-personalized-shopping-experiences-via-chatbot\u002F) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_9a9895dd4cf7.png\" height=\"80px\"> | [Markable.AI](https:\u002F\u002Fmarkable.ai\u002F)                          | 2016  | Recognition, Searching                      | [[2018.7 journal sentinel]](https:\u002F\u002Fwww.jsonline.com\u002Fstory\u002Fmoney\u002Fbusiness\u002F2018\u002F07\u002F26\u002Fshazam-fashion-markable-app-shops-comparable-outfits\u002F816175002\u002F) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_c60b3d4e93d9.png\" height=\"50px\">       | [衣呼 YIHU (TOZI)](https:\u002F\u002Fwww.emtailor.com\u002Fsolutions)       | 2017  | 3D Modeling                                 | [[2018.9 funding]](https:\u002F\u002Fwww.lieyunwang.com\u002Farchives\u002F447739) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_593a74738790.png\" height=\"20px\">      | [macty.eu](https:\u002F\u002Fwww.macty.eu\u002F)                            | 2017  | Recognition, Searching, Recommendation, NLP | [[2018.12 START IT]](https:\u002F\u002Fstartitkbc.prezly.com\u002Fcomplete-the-look-tool-macty-leverages-ai-to-revolutionise-the-fashion-industry) |\n|                                              | [极睿 infimind](http:\u002F\u002Finfimind.com\u002F)                        | 2017  |                                             |                                                              |\n|                                              | [知衣 zhiyi](https:\u002F\u002Fwww.zhiyitech.cn\u002F)                      | 2018  |                                             |                                                              |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_7f96c9c49720.gif\" height=\"30px\">     | [glitch-ai](https:\u002F\u002Fglitch-ai.com\u002F)                          | 2019  | AI Design                                   | [[2019.6 news]](https:\u002F\u002Fwww.vice.com\u002Fen_us\u002Farticle\u002Fvb9pgm\u002Fthis-clothing-line-was-designed-by-ai) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_df19f96eb4fd.png\" height=\"30px\">  | [深尚科技 StylingAI](https:\u002F\u002Fshenshangtech.com\u002F)             | 2019  | AI Design                                   | [[2020.1 news]](https:\u002F\u002F36kr.com\u002Fp\u002F5281004)                  |\n\n\n\n\n\n## Other Useful Resources\n1. [[ayushidalmia\u002Fawesome-fashion-ai]](https:\u002F\u002Fgithub.com\u002Fayushidalmia\u002Fawesome-fashion-ai)\n2. [[lzhbrian\u002Fimage-to-image-papers]](https:\u002F\u002Fgithub.com\u002Flzhbrian\u002Fimage-to-image-papers)\n\n\n\n","# 时尚领域精选论文 👔👗🕶️🎩\n__时尚相关论文与资源（公司、数据集、会议、研讨会等）。__\n\n论文按 arXiv 初稿提交时间排序（如适用）。\n\n欢迎提交 PR 或 Issue。\n\n\n__目录__\n* [论文](#papers)\n    * [合成](#synthesis)\n    * [分类](#classification)\n    * [推荐](#recommendation)\n    * [预测](#forecast)\n* [相关活动](#related-events)\n* [数据集](#datasets)\n* [公司](#companies)\n* [其他实用资源](#other-useful-resources)\n\n\n\n\n## 论文\n### 合成\n| 模型 | 标题 | 发表 | 论文 | 链接 |\n| ----- | ----- | ----------- | ----- | ---- |\n| Pose with style | 姿态风格化：基于条件 StyleGAN 的细节保留姿态引导图像合成 | SIGGRAPH ASIA 2021 | [[2109.06166]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.06166) | [[project]](https:\u002F\u002Fpose-with-style.github.io\u002F) |\n| CIT | CIT：用于虚拟试穿的布料交互 Transformer | arXiv | [[2104.05519]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2104.05519) | [[Amazingren \u002F CIT]](https:\u002F\u002Fgithub.com\u002FAmazingren\u002FCIT) |\n| VITON-HD | VITON-HD：基于错位感知归一化的高分辨率虚拟试穿 | CVPR 2021 | [[2103.16874]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.16874) | [[shadow2496 \u002F VITON-HD]](https:\u002F\u002Fgithub.com\u002Fshadow2496\u002FVITON-HD) |\n| DCTON | 解耦循环一致性实现高度逼真的虚拟试穿 | CVPR 2021 | [[2103.09479]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.09479) | [[ChongjianGE \u002F DCTON]](https:\u002F\u002Fgithub.com\u002FChongjianGE\u002FDCTON) |\n| PF-AFN | 无需解析器的虚拟试穿：通过蒸馏外观流实现 | CVPR 2021 | [[2103.04559]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2103.04559) | [[geyuying \u002F PF-AFN]](https:\u002F\u002Fgithub.com\u002Fgeyuying\u002FPF-AFN) |\n| SieveNet | SieveNet：鲁棒的基于图像的虚拟试穿统一框架 | WACV 2020 | [[2001.06265]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2001.06265) |  |\n|  | 细节至上：带细节雕刻的虚拟试穿 | arXiv | [[1912.06324]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1912.06324) | [[AIprogrammer \u002F Down-to-the-Last-Detail-Virtual-Try-on-with-Detail-Carving]](https:\u002F\u002Fgithub.com\u002FAIprogrammer\u002FDown-to-the-Last-Detail-Virtual-Try-on-with-Detail-Carving) |\n| ClothFlow | ClothFlow：基于流模型的着装人物生成 | ICCV 2019 | [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FHan_ClothFlow_A_Flow-Based_Model_for_Clothed_Person_Generation_ICCV_2019_paper.pdf) |  |\n| FW-GAN | FW-GAN：面向视频虚拟试穿的流导航变形 GAN | ICCV 2019 | [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ICCV_2019\u002Fpapers\u002FDong_FW-GAN_Flow-Navigated_Warping_GAN_for_Video_Virtual_Try-On_ICCV_2019_paper.pdf) |  |\n|  | 使用任意姿势虚拟试穿新服装 | MM 2019 | [[paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3350946) | [[project]](https:\u002F\u002Ffashiontryon.wixsite.com\u002Ffashiontryon) |\n|  | 生成穿着定制服装的高分辨率时尚模特图像 | ICCVW 2019 | [[1908.08847]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1908.08847) |  |\n| Fashion++ | Fashion++：用于提升穿搭效果的最小化编辑 | ICCV 2019 | [[1904.09261]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1904.09261) | [[project]](http:\u002F\u002Fvision.cs.utexas.edu\u002Fprojects\u002FFashionPlus\u002F) |\n| MG-VTON | 多姿态引导的虚拟试穿网络 | arXiv | [[1902.11026]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.11026) |  |\n| FiNet | 兼容且多样化的时尚图像修复 | ICCV 2019 | [[1902.01096]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1902.01096) |  |\n| M2E-Try On Net | M2E-Try On Net：从模特到大众的时尚 | arXiv | [[1811.08599]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1811.08599) |  |\n| FashionGAN | FashionGAN：使用条件生成对抗网络展示你的时尚设计 | CG Forum 2018 | [[paper]](https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fabs\u002F10.1111\u002Fcgf.13552) |  |\n| PIVTONS | PIVTONS：具有条件图像补全功能的姿态不变虚拟试鞋 | ACCV 2018 | [[paper]](https:\u002F\u002Fwinstonhsu.info\u002Fwp-content\u002Fuploads\u002F2018\u002F09\u002Fchou18PIVTONS.pdf) | [[project]](https:\u002F\u002Fwinstonhsu.info\u002Fpubs\u002Fpivtons-virtual-try-on-shoe\u002F) |\n| SwapNet | SwapNet：基于图像的服装转移 | ECCV 2018 | [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_ECCV_2018\u002Fpapers\u002FAmit_Raj_SwapNet_Garment_Transfer_ECCV_2018_paper.pdf) | [[andrewjong \u002F SwapNet]](https:\u002F\u002Fgithub.com\u002Fandrewjong\u002FSwapNet) |\n| FiLMedGAN | 基于语言指导和特征变换的时尚图像操控 | ECCVW 2018 | [[1808.04000]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1808.04000) |  |\n| CP-VITON | 致力于保持特征的基于图像的虚拟试穿网络 | ECCV 2018 | [[1807.07688]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.07688) | [[sergeywong \u002F cp-vton]](https:\u002F\u002Fgithub.com\u002Fsergeywong\u002Fcp-vton) |\n|  | 在 GAN 中解耦多个条件输入 | ECCVW 2018 | [[1806.07819]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.07819) | [[zalandoresearch \u002F disentangling_conditional_gans]](https:\u002F\u002Fgithub.com\u002Fzalandoresearch\u002Fdisentangling_conditional_gans) |\n| DesIGN | DesIGN：来自生成网络的设计灵感 | ECCVW 2018 | [[1804.00921]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.00921) |  |\n| VITON | VITON：基于图像的虚拟试穿网络 | CVPR 2018 | [[1711.08447]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.08447) | [[xthan \u002F VITON]](https:\u002F\u002Fgithub.com\u002Fxthan\u002FVITON) |\n| DVBPR | 基于生成式图像模型的视觉感知时尚推荐与设计 | ICDM 2017 | [[1711.02231]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1711.02231) | [[kang205 \u002F DVBPR]](https:\u002F\u002Fgithub.com\u002Fkang205\u002FDVBPR) |\n| FashionGAN | 成为你自己的 Prada：具有结构一致性的时尚合成。 | ICCV 2017 | [[1710.07346]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.07346) | [[project]](http:\u002F\u002Fmmlab.ie.cuhk.edu.hk\u002Fprojects\u002FFashionGAN\u002F) |\n| CAGAN | 条件类比 GAN：在人物图像上交换时尚单品 | ICCVW 2017 | [[1709.04695]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1709.04695) |  |\n\n### 分类\n| 模型 | 标题 | 发表 | 论文 | 链接 |\n| ----- | ----- | ----------- | ----- | ---- |\n| DeepFashion2 | DeepFashion2：服装图像检测、姿态估计、分割和重识别的多功能基准 | CVPR 2019 | [[1901.07973]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1901.07973) | [[switchablenorms \u002F DeepFashion2]](https:\u002F\u002Fgithub.com\u002Fswitchablenorms\u002FDeepFashion2) |\n|  | 品牌 > 标志：时尚品牌的视觉分析 | ECCVW 2018 | [[1810.09941]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1810.09941) |  |\n| BCRNN | 用于时尚关键点检测和服装类别分类的注意力时尚语法网络 | CVPR 2018 | [[paper]](http:\u002F\u002Fopenaccess.thecvf.com\u002Fcontent_cvpr_2018\u002Fpapers\u002FWang_Attentive_Fashion_Grammar_CVPR_2018_paper.pdf) |  |\n|  | Studio2Shop：从工作室拍摄到时尚商品 | ICPRAM 2018 | [[1807.00556]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1807.00556) |  |\n| FashionBrain | FashionBrain项目：理解欧洲时尚数据宇宙的愿景 | KDDW 2017 | [[1710.09788]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1710.09788) | [[project]](https:\u002F\u002Ffashionbrain-project.eu\u002F) |\n|  | 自动的空间感知时尚概念发现 | ICCV 2017 | [[1708.01311]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.01311) | [[xthan \u002F fashion-200k]](https:\u002F\u002Fgithub.com\u002Fxthan\u002Ffashion-200k) |\n| DFA | 野外时尚关键点检测 | ECCV 2016 | [[1608.03049]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1608.03049) | [[liuziwei7 \u002F fashion-landmarks]](https:\u002F\u002Fgithub.com\u002Fliuziwei7\u002Ffashion-landmarks) |\n| FashionNet | DeepFashion：通过丰富的标注支持鲁棒的服装识别与检索 | CVPR 2016 | [[paper]](http:\u002F\u002Fwww.cv-foundation.org\u002Fopenaccess\u002Fcontent_cvpr_2016\u002Fpapers\u002FLiu_DeepFashion_Powering_Robust_CVPR_2016_paper.pdf) | [[project]](https:\u002F\u002Fliuziwei7.github.io\u002Fprojects\u002FDeepFashion.html) |\n\n\n\n### 推荐\n| 模型 | 标题 | 发表 | 论文 | 链接 |\n| ----- | ----- | ----------- | ----- | ---- |\n|  | 用于时尚搭配的半监督视觉表示学习 | RecSys 2021 | [[2109.08052]](https:\u002F\u002Farxiv.org\u002Fabs\u002F2109.08052) |  |\n| POG | POG：阿里巴巴iFashion中的个性化穿搭生成 | KDD 2019 | [[1905.01866]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1905.01866) |  |\n|  | 基于美学的服装推荐 | WWW 2018 | [[1809.05822]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1809.05822) |  |\n| CRAFT | CRAFT：利用对抗性特征转换器进行互补推荐 | ECCVW 2018 | [[1804.10871]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1804.10871) |  |\n|  | 学习类型感知嵌入以实现时尚搭配 | ECCV 2018 | [[1803.09196]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1803.09196) |  |\n| NeuroStylist | NeuroStylist：用于服装搭配的神经网络兼容性建模 | MM 2017 | [[paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3123314) |  |\n|  | 深度跨领域时尚推荐 | RecSys 2017 | [[paper]](https:\u002F\u002Fdl.acm.org\u002Fcitation.cfm?id=3109861) |  |\n|  | 基于LSTM的动态客户模型用于时尚推荐 | RecSys 2017 | [[1708.07347]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1708.07347) |  |\n|  | 使用双向LSTM学习时尚搭配 | MM 2017 | [[1707.05691]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.05691) | [[xthan \u002F polyvore]](https:\u002F\u002Fgithub.com\u002Fxthan\u002Fpolyvore) |\n|  | 时尚DNA：融合内容与销售数据以进行推荐和商品映射 | KDD 2016 | [[1609.02489]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1609.02489) |  |\n\n\n\n### 预测\n| 模型 | 标题 | 发表 | 论文 | 链接 |\n| ----- | ----- | ----------- | ----- | ---- |\n| Style Quotient | 理解时尚度：是什么驱动了某种风格的销售？ | KDDW 2018 | [[1806.11424]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1806.11424) |  |\n| Sales Potential | 销售潜力：建模时尚产品视觉美学的可销售性 | KDDW 2017 | [[paper]](https:\u002F\u002Fkddfashion2017.mybluemix.net\u002Ffinal_submissions\u002FML4Fashion_paper_10.pdf) |  |\n|  | 时尚前沿：预测时尚中的视觉风格 | ICCV 2017 | [[1705.06394]](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.06394) |  |\n\n\n\n\n\n\n## 相关活动\n1. KDD时尚研讨会 [[2019]](https:\u002F\u002Fkddfashion2019.mybluemix.net\u002F) [[2018]](https:\u002F\u002Fkddfashion2018.mybluemix.net\u002F) [[2017]](https:\u002F\u002Fkddfashion2017.mybluemix.net\u002F) [[2016]](http:\u002F\u002Fkddfashion2016.mybluemix.net\u002F)\n2. 计算机视觉在时尚、艺术和设计中的研讨会 [[CVPR 2020]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fcvcreative2020) [[ICCV 2019]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fcvcreative\u002Fhome) [[ECCV 2018]](https:\u002F\u002Fsites.google.com\u002Fview\u002Feccvfashion\u002F) [[ICCV 2017]](https:\u002F\u002Fsites.google.com\u002Fzalando.de\u002Fcvf-iccv2017\u002Fhome?authuser=0)\n3. NeurlPS创意与设计机器学习研讨会 [[2019]](https:\u002F\u002Fneurips2019creativity.github.io\u002F) [[2018]](https:\u002F\u002Fnips2018creativity.github.io\u002F) [[2017]](https:\u002F\u002Fnips2017creativity.github.io\u002F)\n4. SIGIR电子商务研讨会 [[2019]](https:\u002F\u002Fsigir-ecom.github.io\u002Findex.html) [[2018]](https:\u002F\u002Fsigir-ecom.github.io\u002Fecom2018\u002Findex.html) [[2017]](http:\u002F\u002Fsigir-ecom.weebly.com\u002F) \n5. CVPR内容创作深度学习教程 [[2019]](https:\u002F\u002Fnvlabs.github.io\u002Fdl-for-content-creation\u002F)\n6. iMaterialist时尚挑战赛 [[CVPR 2019]](https:\u002F\u002Fsites.google.com\u002Fview\u002Ffgvc6\u002Fcompetitions\u002Fimat-fashion-2019)\n7. iDesigner挑战赛 [[CVPR 2019]](https:\u002F\u002Fsites.google.com\u002Fview\u002Ffgvc6\u002Fcompetitions\u002Fidesigner-2019)\n8. FashionGen挑战赛 [[ICCV 2019, ECCV 2018]](https:\u002F\u002Ffashion-gen.com\u002F)\n9. JD AI时尚挑战赛 [[ChinaMM 2018]](https:\u002F\u002Ffashion-challenge.github.io\u002F)\n10. 阿里巴巴FashionAI全球挑战赛 [[Tianchi]](http:\u002F\u002Ffashionai.alibaba.com\u002F)\n11. 时尚与纺织人工智能会议 [[AIFT 2018]](https:\u002F\u002Fwww.polyu.edu.hk\u002Fitc\u002Faift2018\u002F)\n12. Fashion IQ挑战赛 [[CVPR 2020]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fcvcreative2020\u002Ffashion-iq?authuser=0) [[ICCV 2019]](https:\u002F\u002Fsites.google.com\u002Fview\u002Flingir\u002Ffashion-iq)\n13. DeepFashion2挑战赛 [[CVPR 2020]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fcvcreative2020\u002Fdeepfashion2?authuser=0) [[ICCV 2019]](https:\u002F\u002Fsites.google.com\u002Fview\u002Fcvcreative\u002Fdeepfashion2)\n\n\n\n\n## 数据集\n1. Fashionpedia [[网站]](https:\u002F\u002Ffashionpedia.github.io\u002Fhome\u002Findex.html)\n2. DeepFashion2数据集 [[网站]](\u003Chttps:\u002F\u002Fgithub.com\u002Fswitchablenorms\u002FDeepFashion2>)\n3. DeepFashion数据集 [[网站]](http:\u002F\u002Fmmlab.ie.cuhk.edu.hk\u002Fprojects\u002FDeepFashion.html)\n4. FashionGen [[网站]](https:\u002F\u002Ffashion-gen.com\u002F)\n5. FashionAI [[Tianchi]](http:\u002F\u002Ffashionai.alibaba.com\u002Fdatasets\u002F?spm=a2c22.11190735.991137.8.501b6d83ilPJsX)\n6. TaobaoClothMatch [[Tianchi]](TaobaoClothMatch)\n7. Fashion-MNIST [[zalandoresearch\u002Ffashion-mnist]](https:\u002F\u002Fgithub.com\u002Fzalandoresearch\u002Ffashion-mnist)\n8. Fashion IQ [[网站]](https:\u002F\u002Fwww.spacewu.com\u002Fposts\u002Ffashion-iq\u002F)\n\n## 企业\n\n| 品牌                                        | 名称                                                         | 成立年份 | 信息                                        | 新闻                                                         |\n| -------------------------------------------- | ------------------------------------------------------------ | -------- | ------------------------------------------- | ------------------------------------------------------------ |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_8b18aee6f357.png\" height=\"80px\">     | [Myntra](https:\u002F\u002Fwww.myntra.com\u002F)                            | 2007     | 预测、合成                                  | [[2017.11 livemint]](https:\u002F\u002Fwww.livemint.com\u002F)              |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_4b6a95e4ed90.png\" height=\"50px\">    | [阿里巴巴 图像和美](https:\u002F\u002Fwww.leiphone.com\u002FaiWeights\u002Flab\u002F79) | 2009     | 识别                                        | [[2018.7 FashionAI]](https:\u002F\u002Fwww.leiphone.com\u002Fnews\u002F201807\u002FBp6UlbXIab29rIN6.html) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_ea0d98c5baf1.png\" width=\"200px\">  | [STITCH FIX](https:\u002F\u002Fwww.stitchfix.com\u002F)、[BLOG](https:\u002F\u002Fmultithreaded.stitchfix.com\u002Fblog\u002F) | 2011     | 个性化                                    | [[2018.5 Forbes]](https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fbernardmarr\u002F2018\u002F05\u002F25\u002Fstitch-fix-the-amazing-use-case-of-using-artificial-intelligence-in-fashion-retail\u002F#5d5ff05c3292) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_773060603735.png\" width=\"200px\">  | [Heuritech](https:\u002F\u002Fwww3.heuritech.com\u002F)                     | 2013     | 预测、识别                                  | [[2019.1 Fashnerd]](https:\u002F\u002Ffashnerd.com\u002F2019\u002F01\u002Ffrench-startup-heuritech-wants-to-help-fashion-brands-make-clothes-that-customers-want\u002F) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_14ab29b07731.png\" height=\"50px\">      | [Yi+] (http:\u002F\u002Fwww.dressplus.cn\u002Fhome)                          | 2014     | 识别                                        | [[2018.8 融资]](https:\u002F\u002Fpe.pedaily.cn\u002F201808\u002F434505.shtml) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_8873772e0ea0.png\" height=\"80px\">     | [MALONG TECHNOLOGIES](http:\u002F\u002Fwww.malong.com\u002Fen\u002Fhome)         | 2014     | 识别                                        | [[2018.7 Forbes]](https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fbernardmarr\u002F2018\u002F07\u002F09\u002F14-incredible-artificial-intelligence-pioneers-everyone-should-know-about\u002F#7a23aaaa5626) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_60b4df319c62.png\" height=\"80px\">       | [syte](https:\u002F\u002Fwww.syte.ai\u002F)                                 | 2015     | 识别                                        | [[2018.12 与 farfetch 合作]](https:\u002F\u002Ftechstartups.com\u002F2018\u002F12\u002F07\u002Fvisual-ai-startup-syte-partners-luxury-fashion-platform-farfetch-launch-new-app-visual-search-feature-ios\u002F) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_18255f6703cd.png\" width=\"200px\">  | [GrokStyle](https:\u002F\u002Fwww.grokstyle.com\u002F)（2019.2 被 [Facebook](https:\u002F\u002Fwww.facebook.com\u002F) 收购） | 2015     | 搜索                                        | [[2019.2 被 Facebook 收购]](https:\u002F\u002Fventurebeat.com\u002F2019\u002F02\u002F08\u002Ffacebook-acquires-visual-search-startup-grokstyle\u002F) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_8fbbd443d71b.png\" width=\"200px\">    | [Zalando Research](https:\u002F\u002Fresearch.zalando.com\u002F)            | 2016     | 研究                                        | [[2016.10 成立]](https:\u002F\u002Fearlymoves.com\u002F2016\u002F10\u002F07\u002Fzalando-research-is-shaping-the-future-of-online-fashion\u002F) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_99c15508f3c2.png\" width=\"200px\">     | [MatchU 码尚](https:\u002F\u002Fwww.immatchu.com\u002F)                     | 2016     | 建模                                        | [[2018.12 融资]](http:\u002F\u002Fwww.iheima.com\u002Farticle-195955.html) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_f4e6801b799a.png\" width=\"200px\">     | [mode.ai](https:\u002F\u002Fmode.ai\u002F#\u002Fabout)                           | 2016     | 识别、NLP、搜索                             | [[2018.5 TechRepublic]](https:\u002F\u002Fwww.techrepublic.com\u002Farticle\u002Fhow-mode-ai-powers-personalized-shopping-experiences-via-chatbot\u002F) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_9a9895dd4cf7.png\" height=\"80px\"> | [Markable.AI](https:\u002F\u002Fmarkable.ai\u002F)                          | 2016     | 识别、搜索                                  | [[2018.7 journal sentinel]](https:\u002F\u002Fwww.jsonline.com\u002Fstory\u002Fmoney\u002Fbusiness\u002F2018\u002F07\u002F26\u002Fshazam-fashion-markable-app-shops-comparable-outfits\u002F816175002\u002F) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_c60b3d4e93d9.png\" height=\"50px\">       | [衣呼 YIHU (TOZI)](https:\u002F\u002Fwww.emtailor.com\u002Fsolutions)       | 2017     | 3D 建模                                     | [[2018.9 融资]](https:\u002F\u002Fwww.lieyunwang.com\u002Farchives\u002F447739) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_593a74738790.png\" height=\"20px\">      | [macty.eu](https:\u002F\u002Fwww.macty.eu\u002F)                            | 2017     | 识别、搜索、推荐、NLP                       | [[2018.12 START IT]](https:\u002F\u002Fstartitkbc.prezly.com\u002Fcomplete-the-look-tool-macty-leverages-ai-to-revolutionise-the-fashion-industry) |\n|                                              | [极睿 infimind](http:\u002F\u002Finfimind.com\u002F)                        | 2017     |                                             |                                                              |\n|                                              | [知衣 zhiyi](https:\u002F\u002Fwww.zhiyitech.cn\u002F)                      | 2018     |                                             |                                                              |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_7f96c9c49720.gif\" height=\"30px\">     | [glitch-ai](https:\u002F\u002Fglitch-ai.com\u002F)                          | 2019     | AI 设计                                     | [[2019.6 新闻]](https:\u002F\u002Fwww.vice.com\u002Fen_us\u002Farticle\u002Fvb9pgm\u002Fthis-clothing-line-was-designed-by-ai) |\n| \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_readme_df19f96eb4fd.png\" height=\"30px\">  | [深尚科技 StylingAI](https:\u002F\u002Fshenshangtech.com\u002F)             | 2019     | AI 设计                                     | [[2020.1 新闻]](https:\u002F\u002F36kr.com\u002Fp\u002F5281004)                  |\n\n\n\n\n\n## 其他有用资源\n1. [[ayushidalmia\u002Fawesome-fashion-ai]](https:\u002F\u002Fgithub.com\u002Fayushidalmia\u002Fawesome-fashion-ai)\n2. [[lzhbrian\u002Fimage-to-image-papers]](https:\u002F\u002Fgithub.com\u002Flzhbrian\u002Fimage-to-image-papers)","# Cool-Fashion-Papers 快速上手指南\n\n**Cool-Fashion-Papers** 并非一个单一的代码库或可执行工具，而是一个**时尚领域 AI 论文、数据集、相关会议及公司的精选资源列表**。它旨在为研究人员和开发者提供该领域的最新进展索引。\n\n因此，本指南将指导你如何获取该资源列表，并演示如何利用列表中的信息快速启动一个典型的时尚 AI 项目（以虚拟试穿模型为例）。\n\n## 环境准备\n\n由于本项目是资源索引，无需安装特定的主程序。但若要运行列表中引用的具体模型（如 VITON-HD, CIT 等），通常需要以下通用环境：\n\n*   **操作系统**: Linux (推荐 Ubuntu 18.04\u002F20.04) 或 macOS\n*   **Python**: 3.7 或更高版本\n*   **深度学习框架**: PyTorch (大多数时尚生成模型基于此)\n*   **硬件**: 建议使用支持 CUDA 的 NVIDIA GPU (显存建议 8GB 以上，高分辨率模型需 16GB+)\n*   **依赖管理**: `pip` 或 `conda`\n\n## 安装步骤\n\n### 1. 获取资源列表\n首先，克隆该仓库到本地以浏览完整的论文和代码链接：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fsergeywong\u002Fcool-fashion-papers.git\ncd cool-fashion-papers\n```\n\n### 2. 选择并安装具体模型\n在 `README.md` 的 **Papers** 部分找到你感兴趣的模型（例如 **VITON-HD**），点击其对应的 GitHub 链接进入项目主页。\n\n以 **VITON-HD** (高分辨率虚拟试穿) 为例，安装步骤如下：\n\n```bash\n# 克隆具体模型代码库\ngit clone https:\u002F\u002Fgithub.com\u002Fshadow2496\u002FVITON-HD.git\ncd VITON-HD\n\n# 创建虚拟环境 (推荐)\nconda create -n viton-hd python=3.8\nconda activate viton-hd\n\n# 安装 PyTorch (根据官方推荐版本，此处以 CUDA 11.1 为例)\n# 国内用户推荐使用清华源加速\npip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Ftorch_stable.html\npip install -r requirements.txt -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n```\n\n*(注：不同模型的具体依赖请参考各自仓库的 `requirements.txt`)*\n\n## 基本使用\n\n由于 Cool-Fashion-Papers 本身是索引，\"使用\"它意味着利用它找到工具并运行。以下是基于列表中 **Synthesis (图像合成)** 类别的典型工作流示例。\n\n### 示例：运行虚拟试穿 (Virtual Try-On)\n\n假设你已按照上述步骤安装了 **VITON-HD**，以下是推理测试的基本流程：\n\n1.  **准备数据**：下载预训练模型和数据集（通常在模型主页提供链接）。\n2.  **执行推理**：\n\n```bash\n# 进入项目目录\ncd VITON-HD\n\n# 运行测试脚本 (参数需根据具体模型文档调整)\npython test.py --dataroot .\u002Fdataset\u002Fexamples --name viton_hd --stage 2 --which_model_netG latest\n```\n\n### 如何探索更多资源\n\n你可以直接在本地查看 `README.md` 文件，利用目录结构快速定位需求：\n\n*   **图像生成\u002F试穿**: 查看 `Papers` -> `Synthesis` 章节（包含 VITON, CP-VTON, SwapNet 等）。\n*   **分类与检测**: 查看 `Papers` -> `Classification` 章节（包含 DeepFashion2, FashionLandmark 等）。\n*   **推荐系统**: 查看 `Papers` -> `Recommendation` 章节。\n*   **数据集下载**: 查看 `Datasets` 章节，获取 Fashionpedia, DeepFashion 等数据源的官方链接。\n*   **学术会议**: 查看 `Related Events` 章节，追踪 KDD, CVPR, ICCV 等会议上的时尚专题研讨会。\n\n通过该列表，你可以直接跳转到对应论文的 arXiv 链接阅读细节，或跳转到 GitHub 链接获取源码。","某时尚科技初创公司的算法团队正致力于研发一款高保真“虚拟试衣”功能，旨在让用户上传照片即可预览不同服装的上身效果。\n\n### 没有 Cool-Fashion-Papers 时\n- **检索效率低下**：工程师需在 arXiv、Google Scholar 等多个平台分散搜索\"Virtual Try-On\"相关论文，耗时数天仍难以覆盖最新成果。\n- **复现门槛过高**：找到的论文往往缺乏对应的开源代码链接，或仓库已失效，导致算法验证和对比实验无法启动。\n- **技术选型盲目**：由于缺乏按任务（如合成、分类）分类的清晰指引，团队难以判断该采用基于 Flow 的模型还是 GAN 架构，容易选错技术路线。\n- **资源碎片化**：数据集、行业会议和相关公司信息散落在各处，难以形成完整的技术生态视图，阻碍了产品落地的整体规划。\n\n### 使用 Cool-Fashion-Papers 后\n- **一站式获取前沿成果**：团队直接查阅按时间排序的论文列表，迅速锁定了 CVPR 2021 的 VITON-HD 和 DCTON 等高分辨率试衣最新方案。\n- **代码复现零障碍**：每个模型条目均附带官方 GitHub 链接（如 CIT、PF-AFN），开发人员当天即可拉取代码进行基线测试。\n- **精准技术决策**：借助清晰的目录分类（Synthesis\u002FClassification 等），团队快速对比了不同模型在“姿态保持”和“细节生成”上的优劣，确定了最优架构。\n- **生态资源整合**：通过关联的数据集和公司板块，团队不仅找到了训练数据，还了解了竞品动态，加速了从研发到商业化的进程。\n\nCool-Fashion-Papers 将原本需要数周的文献调研与资源收集工作压缩至几小时，成为时尚 AI 领域开发者不可或缺的效率加速器。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Flzhbrian_Cool-Fashion-Papers_4505357d.png","lzhbrian","Tzu-Heng Lin","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Flzhbrian_665422a4.jpg","https:\u002F\u002Flzhbrian.me",null,"lzhbrian@gmail.com","https:\u002F\u002Fgithub.com\u002Flzhbrian",[83,87],{"name":84,"color":85,"percentage":86},"Shell","#89e051",76.5,{"name":88,"color":89,"percentage":90},"Python","#3572A5",23.5,629,109,"2026-03-10T16:26:23","MIT",5,"","未说明",{"notes":99,"python":97,"dependencies":100},"该仓库（Cool-Fashion-Papers）是一个时尚领域相关论文、数据集、会议和资源的列表合集，本身不是一个可独立运行的 AI 模型或软件工具，因此 README 中未包含具体的运行环境需求（如操作系统、GPU、内存、Python 版本或依赖库）。用户若需运行列表中提到的具体模型（如 VITON-HD, CIT 等），需前往各模型对应的独立项目链接查看其特定的环境配置要求。",[],[14,54,13],[103,104,105,106,107,108,109,110,111,112,113,114],"fashion","gan","deep-learning","artificial-intelligence","neural-network","generative-adversarial-network","curated-list","collection","papers","machine-learning","awesome-list","research-paper","2026-03-27T02:49:30.150509","2026-04-06T09:26:05.330198",[],[]]