Residual Guided Dynamic Queries Enable Stroke Efficient Neural Oil Painting

Residual Guided Dynamic Queries Enable Stroke Efficient Neural Oil Painting | 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 Residual Guided Dynamic Queries Enable Stroke Efficient Neural Oil Painting Quan Zhou, Peng Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9398429/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Automatic oil painting aims to reconstruct a target image with a compact sequence of executable brushstrokes, yet current neural painters often waste strokes on regions that already match the target and therefore miss subtle structures that still require refinement.We tackle this problem by introducing an explicit residual signal, computed from the current canvas and the target image, so that the model can identify where new brushstrokes are truly needed.Based on this idea, we develop a Differential Query Transformer (DQ-Transformer) that extracts position-aware features from the canvas, the target image, and their residual, and then uses residual features as dynamic queries for stroke decoding.The training objective combines pixel reconstruction, stroke-level supervision, and adversarial learning, which encourages accurate geometry, compact stroke programs, and more convincing painterly texture.Experiments on Landscapes, FFHQ, and WikiArt, together with a user study, show that the proposed method achieves stronger reconstruction quality and higher user preference than competitive baselines while retaining the fast inference behavior of feed-forward neural painters. Neural Painting Stroke-based Rendering Oil Painting Synthesis Dynamic Query Transformer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers invited by journal 22 Apr, 2026 Editor assigned by journal 22 Apr, 2026 Editor invited by journal 17 Apr, 2026 Submission checks completed at journal 17 Apr, 2026 First submitted to journal 17 Apr, 2026 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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