Dream Your Pose: Robust Human Pose Generation via Uncertainty-Aware Structural Reward Modeling

Dream Your Pose: Robust Human Pose Generation via Uncertainty-Aware Structural Reward Modeling | 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 Dream Your Pose: Robust Human Pose Generation via Uncertainty-Aware Structural Reward Modeling Deliang Zhang, Qingqin Wang, Zehua Zhang, Songyin Dai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8439566/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 Conditional diffusion models offer a versatile paradigm for controllable image synthesis, yet faithfully adhering to intricate spatial constraints, such as human pose skeletons, continues to pose significant challenges. Despite progress in control architectures and reward-guided fine-tuning, outputs frequently exhibit structural aberrations, joint misalignments, and anatomically infeasible configurations. We contend that these shortcomings arise primarily from two core deficiencies: pixel-based rewards inadequately encapsulate the perceptual and topological nuances of skeletal forms, and reward signals grow unreliable amid diverse or out-of-distribution samples.To address this, we introduce Dream Your Pose , a perceptually informed framework for pose-conditioned generation that prioritizes structural fidelity. Our method incorporates a multi-channel, structure-sensitive reward mechanism, harnessing perceptual features like local contrast, edge gradients, and spatial continuity to more accurately gauge pose congruence. Critically, we integrate an uncertainty-aware regularization paradigm—drawing from principles of uncertainty modeling in learning—to adaptively modulate reward influence, thereby mitigating the effects of spurious or ambiguous feedback and fostering robust training dynamics.Rigorous evaluations on the OpenPose-ControlNet dataset reveal substantial gains, including a 25.3% relative uplift in Object Keypoint Similarity (OKS) and a 15.6% enhancement in Probability of Correct Keypoint at 0.5 ( [email protected] ), underscoring improved keypoint precision and holistic skeletal integrity. These advancements yield images with superior visual coherence and anatomical plausibility, without compromising semantic fidelity or perceptual quality. Conditional Diffusion Models Pose-Conditioned Image Generation Structure-Aware Rewards Uncertainty Modeling in Learning Controllable Human Pose Synthesis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Mar, 2026 Reviews received at journal 05 Feb, 2026 Reviews received at journal 05 Feb, 2026 Reviews received at journal 04 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviews received at journal 04 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviewers agreed at journal 31 Jan, 2026 Reviewers agreed at journal 30 Jan, 2026 Reviewers invited by journal 21 Jan, 2026 Editor assigned by journal 26 Dec, 2025 Submission checks completed at journal 26 Dec, 2025 First submitted to journal 24 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-8439566","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":578628588,"identity":"7eb30cb1-15c3-4de8-9e27-9fb596c28af9","order_by":0,"name":"Deliang Zhang","email":"","orcid":"","institution":"Beijing Union University","correspondingAuthor":false,"prefix":"","firstName":"Deliang","middleName":"","lastName":"Zhang","suffix":""},{"id":578628591,"identity":"013bd8c1-8727-48d1-99da-51265f4db263","order_by":1,"name":"Qingqin Wang","email":"","orcid":"","institution":"Science and Technology Innovation 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Despite progress in control architectures and reward-guided fine-tuning, outputs frequently exhibit structural aberrations, joint misalignments, and anatomically infeasible configurations. We contend that these shortcomings arise primarily from two core deficiencies: pixel-based rewards inadequately encapsulate the perceptual and topological nuances of skeletal forms, and reward signals grow unreliable amid diverse or out-of-distribution samples.To address this, we introduce \u003cem\u003eDream Your Pose\u003c/em\u003e, a perceptually informed framework for pose-conditioned generation that prioritizes structural fidelity. Our method incorporates a multi-channel, structure-sensitive reward mechanism, harnessing perceptual features like local contrast, edge gradients, and spatial continuity to more accurately gauge pose congruence. 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