Human-Machine Collaborative Model for Dynamic Translation of Intangible Cultural Heritage Patterns Based on Generative Adversarial Network and Reinforcement Learning from Human Feedback

Human-Machine Collaborative Model for Dynamic Translation of Intangible Cultural Heritage Patterns Based on Generative Adversarial Network and Reinforcement Learning from Human Feedback | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Human-Machine Collaborative Model for Dynamic Translation of Intangible Cultural Heritage Patterns Based on Generative Adversarial Network and Reinforcement Learning from Human Feedback Ke Ma, Lin Qi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8484987/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract At present, there is a lack of cultural and artistic expression in the modern design transformation of Intangible Cultural Heritage (ICH) patterns. For this reason, this study proposes a dynamic translation human-machine collaborative model that combines the Generative Adversarial Network (GAN) with Feedback Learning from Human Feedback (RLHF), with the aim of improving the cultural accuracy and quality of the generation of ICH patterns by means of human-machine collaborative. The model includes the content and style extraction module based on conditional Generative Adversarial Network (cGAN) and RLHF mechanism. The former is used to separate and extract the structure and aesthetic features of patterns, while the latter uses reward model to learn expert preferences, and selects near-end strategy to optimize Proximal Policy Optimization (PPO) algorithm to iteratively optimize the generation process. The model is verified on a dataset containing a large number of embroidery images, and it is found that the objective index of the model is better than that of the contrast model, with Fréchet Incidence Distance (FID) of 28.5 and Learned Perceptual Image Patch Similarity (LPIPS) of 0.162. In the subjective evaluation, the score of cultural fit is 4.35 (5-point scale), the designer's task completion time is shortened, and the cognitive load is reduced from 6.2 to 3.1. The above results show that the proposed model can effectively transform the non-legacy patterns with high quality, and strengthen the content style while maintaining the basic cultural characteristics of the patterns, which provides certain technical ideas for the cultural inheritance and innovation of man-machine collaboration. Intangible cultural heritage Pattern generation Generative adversarial network Reinforcement learning from human feedback Human-machine collaborative Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 19 Feb, 2026 Reviews received at journal 12 Feb, 2026 Reviews received at journal 06 Feb, 2026 Reviews received at journal 03 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviewers agreed at journal 01 Feb, 2026 Reviewers agreed at journal 01 Feb, 2026 Reviewers agreed at journal 29 Jan, 2026 Reviewers invited by journal 29 Jan, 2026 Editor invited by journal 20 Jan, 2026 Editor assigned by journal 02 Jan, 2026 Submission checks completed at journal 02 Jan, 2026 First submitted to journal 30 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8484987","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583743958,"identity":"b18b84f7-4932-491e-96d2-6801b80d5514","order_by":0,"name":"Ke Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACgxvJBx8k/JCw42dmPvyAKC2WN44lG3zssUmWbGdLMyBKi/3NHjPBGWxpjBvO8yhIEKXF7DZbGjMPz2Fm48M8DAYMNTbRhLXcYz72mMfiMJ/ZYd4DDxiOpeU2ENbClm4MssXsMF+CAWPDYcJaDG7zmEnzsB1m3NzMYyBBnBag9yXB3mcmWgsskCUOAwM5gRi/IKKy//DhBx9qbAhrQQUJpCkfBaNgFIyCUYALAAAWVEOzvEmDawAAAABJRU5ErkJggg==","orcid":"","institution":"Zhengzhou University of Economics and Business","correspondingAuthor":true,"prefix":"","firstName":"Ke","middleName":"","lastName":"Ma","suffix":""},{"id":583743961,"identity":"febbb714-3805-44d6-ba19-dcbbb398ea2a","order_by":1,"name":"Lin Qi","email":"","orcid":"","institution":"HeNan Province Information Consultation Designing Research Co., Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Qi","suffix":""}],"badges":[],"createdAt":"2025-12-31 02:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8484987/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8484987/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[],"financialInterests":"No competing interests reported.","formattedTitle":"Human-Machine Collaborative Model for Dynamic Translation of Intangible Cultural Heritage Patterns Based on Generative Adversarial Network and Reinforcement Learning from Human Feedback","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Intangible cultural heritage, Pattern generation, Generative adversarial network, Reinforcement learning from human feedback, Human-machine collaborative","lastPublishedDoi":"10.21203/rs.3.rs-8484987/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8484987/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAt present, there is a lack of cultural and artistic expression in the modern design transformation of Intangible Cultural Heritage (ICH) patterns. For this reason, this study proposes a dynamic translation human-machine collaborative model that combines the Generative Adversarial Network (GAN) with Feedback Learning from Human Feedback (RLHF), with the aim of improving the cultural accuracy and quality of the generation of ICH patterns by means of human-machine collaborative. The model includes the content and style extraction module based on conditional Generative Adversarial Network (cGAN) and RLHF mechanism. The former is used to separate and extract the structure and aesthetic features of patterns, while the latter uses reward model to learn expert preferences, and selects near-end strategy to optimize Proximal Policy Optimization (PPO) algorithm to iteratively optimize the generation process. The model is verified on a dataset containing a large number of embroidery images, and it is found that the objective index of the model is better than that of the contrast model, with Fr\u0026eacute;chet Incidence Distance (FID) of 28.5 and Learned Perceptual Image Patch Similarity (LPIPS) of 0.162. In the subjective evaluation, the score of cultural fit is 4.35 (5-point scale), the designer's task completion time is shortened, and the cognitive load is reduced from 6.2 to 3.1. The above results show that the proposed model can effectively transform the non-legacy patterns with high quality, and strengthen the content style while maintaining the basic cultural characteristics of the patterns, which provides certain technical ideas for the cultural inheritance and innovation of man-machine collaboration.\u003c/p\u003e","manuscriptTitle":"Human-Machine Collaborative Model for Dynamic Translation of Intangible Cultural Heritage Patterns Based on Generative Adversarial Network and Reinforcement Learning from Human Feedback","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 13:14:03","doi":"10.21203/rs.3.rs-8484987/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-20T04:00:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-13T02:34:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T14:26:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-04T01:40:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"14199538154777169572435349437226805301","date":"2026-02-03T06:04:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314539112136697475824772764916874573047","date":"2026-02-02T00:54:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58122345982447349156397899619523685142","date":"2026-02-01T09:49:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185153535072247490602696940271399700288","date":"2026-01-29T13:22:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T09:32:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-20T10:10:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-02T12:06:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-02T12:06:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Artificial Intelligence","date":"2025-12-31T01:52:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7c504049-4d22-4eee-883d-ac7590bfa133","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T03:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 13:14:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8484987","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8484987","identity":"rs-8484987","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}