HyperHuman: Learning pose-independent human avatar with enhanced explicit constriant

HyperHuman: Learning pose-independent human avatar with enhanced explicit constriant | 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 Article HyperHuman: Learning pose-independent human avatar with enhanced explicit constriant Yongang Yu, Zhigang Chen, Likang Cheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7656006/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Animating virtual avatars with free-view control through implicit neural radiance field rendering has attracted considerable attention. Previous studies have attempted to simulate the dynamic changes of the human body by improving the representation of the neural radiance field. A prevalent method employs a pose-dependent representation and explicit motion space constraints to animate both rigid and non-rigid, vivid human motion. However, pose-driven deformation faces challenges in modeling explicit mesh topology. Topological changes need continuous deformation fields that accurately reflect human motion, complicating the detailed rendering of complex human surfaces. In this work, we propose a novel framework Hyper-Human, which lifts the deformation field into higher dimensional space while maintaining pose-independent. Our key insight is to model the deformation field with explicit constraints, which explicitly leverage the human surface into a higher dimensional representation. Specifically, we first introduce a closest representation that establishes a pose-independent, generalizable deformation field, anchored by an explicit constraint. Then, we use a deep network to design the moving space as a higher continuous topological transformation field. Extensive experiments demonstrate the superiority of our proposed HyperHuman over state-of-the-art methods, and the ablation study illustrates the effectiveness of our method. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-7656006","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":583421533,"identity":"b436f5f9-69a3-48c2-b6ca-24cc228a6897","order_by":0,"name":"Yongang Yu","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yongang","middleName":"","lastName":"Yu","suffix":""},{"id":583421535,"identity":"0c83b312-424f-4634-82ba-b1a67e2d0a3c","order_by":1,"name":"Zhigang Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYHACxgcQOoF4LcwGJGthkyBNC3//8WsVP/4cZuBnzzFg+LmDCC0SB86U3extO8wg2fPGgLH3DBFaDBh70m4zNhxmMLiRY8DM2EaMFmaetGIGoMPsidfCxn6MmYENaIsEsVokzvAwS/a2pfNInHlWcLCXGC3AEHv44ccfazn+9uSND34So4WBgQcckzwg4gBRGhgY2B8QqXAUjIJRMApGLAAA3y8x4DjWtfAAAAAASUVORK5CYII=","orcid":"","institution":"Central South University","correspondingAuthor":true,"prefix":"","firstName":"Zhigang","middleName":"","lastName":"Chen","suffix":""},{"id":583421541,"identity":"5c2edab1-9b81-448f-ad54-63c1d1507be5","order_by":2,"name":"Likang Cheng","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Likang","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2025-09-19 08:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7656006/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7656006/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104781010,"identity":"fd293cb5-ae21-40a1-8106-cecd532352a7","added_by":"auto","created_at":"2026-03-17 07:54:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1124912,"visible":true,"origin":"","legend":"","description":"","filename":"latexsubmittion.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7656006/v1_covered_041de67a-0420-40bc-8f49-632d35706dcb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"HyperHuman: Learning pose-independent human avatar with enhanced explicit constriant","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7656006/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7656006/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Animating virtual avatars with free-view control through implicit neural radiance field rendering has attracted considerable attention. 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