Higher Performance Full-Body Tracking Method by Integrating Multiple Tracking Techniques Based on Deep Latent Space

Higher Performance Full-Body Tracking Method by Integrating Multiple Tracking Techniques Based on Deep Latent Space | 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 Higher Performance Full-Body Tracking Method by Integrating Multiple Tracking Techniques Based on Deep Latent Space Kazuhiro Esaki, Katashi Nagao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8226996/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 In this paper, we propose the Deep Latent Space Assimilation Model (D-LSAM), a novel framework for integrating multiple body tracking techniques in XR environments to achieve more precise, real-time motion capture. Inside-Out Body Tracking (IOBT) on VR headsets can accurately track upper-body and finger movements, yet it struggles to capture areas outside the camera’s field of view—particularly the lower body. On the other hand, external-camera or smartphone-based systems can observe the entire body but often suffer from delays or reduced accuracy. The D-LSAM addresses these limitations by combining a Wasserstein autoencoder for pose compression, a Transformer-driven Latent Time-Stepping module for movement prediction, and a cross-attention gating mechanism that adaptively fuses data from various sources. Experimental results confirm that the D-LSAM outperforms both the extended Kalman filter and particle filter-based methods in short- to mid-term motion forecasting. Future work will emphasize faster inference, improved handling of rapid movements, and support for a wider range of devices. Progress in this methodology holds promise for delivering more immersive XR applications and for advancing fields such as medicine, sports, and rehabilitation. Body Tracking Motion Capture Data Assimilation Deep Learning Extended Reality 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8226996","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553973923,"identity":"a1b5760c-7a30-4746-a7ff-33a63b363a0b","order_by":0,"name":"Kazuhiro Esaki","email":"","orcid":"","institution":"Nagoya University","correspondingAuthor":false,"prefix":"","firstName":"Kazuhiro","middleName":"","lastName":"Esaki","suffix":""},{"id":553973926,"identity":"3ff9f29d-5a6d-44d4-913d-620f0af8415e","order_by":1,"name":"Katashi 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