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The method addresses stroke ambiguity, variable stroke thickness, and overlapping strokes by using a hierarchical three-stage framework: global semantic stroke-order generation, mid-level human-drawing-mechanics optimization, and low-level perceptual stroke rendering. Keyframes guide global ordering via a diffusion-based model. A stroke renderer extracts pixel-level variable-width substrokes, and human drawing dynamics are modeled with equations to infer sequencing and composition. Extensive experiments and user studies show natural, coherent process animations for high-quality line art.",{"@graph":69,"@context":121},[70,84,104],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/linedrawer-stroke-level-process-reconstruction-of-complex-line-art-based-on-human-perception/674970/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/linedrawer-stroke-level-process-reconstruction-of-complex-line-art-based-on-human-perception/674970.png","ImageObject",300,407,{"name":92,"@type":93},"Bintang","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04",true,{"@type":101,"interactionType":102,"userInteractionCount":4},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"What is the main goal of LineDrawer?","Question",{"text":111,"@type":112},"LineDrawer aims to reconstruct the stroke-by-stroke drawing process from complex line art, enhancing understanding of how such drawings can be reproduced.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"What challenges does the method address in complex line art?",{"text":116,"@type":112},"It tackles stroke ambiguity, variations in stroke thickness, and overlapping strokes that create combinatorial ordering difficulties.",{"name":118,"@type":109,"acceptedAnswer":119},"How does LineDrawer generate the reconstructed drawing sequence?",{"text":120,"@type":112},"It uses a hierarchical framework with three stages: global semantic stroke order from keyframes, mid-level optimization of drawing mechanics, and low-level perceptual stroke rendering for variable-width substrokes.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},674970,1793772021,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":29,"category_name":30,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":4,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":138,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":128,"read_time":143},962085564381,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Computers & Graphics 132 (2025) 104365  \n\n| \u003Cbr>\u003Cbr>Contents lists available at ScienceDirect Computers & Graphics\u003Cbr>journal [homepage:](homepage: www.elsevier.com/locate/cag)[ www.elsevier.com/locate/cag](homepage: www.elsevier.com/locate/cag) | |\n| --- | --- |\n\n\n| Special Section on CAD/Graphics 2025\u003Cbr>LineDrawer: Stroke-level process reconstruction of complex line art based on human perception |  |  | |\n| --- | --- | --- | --- |\n| Zhengyu Huang a, Zhongyue Guan a, Zeyu Wang a,b ,∗ a The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511453, China b The Hong Kong University of Science and Technology, 999077, Hong Kong, China |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Line art\u003Cbr>Stroke-level reconstruction Drawing process inversion Deep learning |  | Line art is a fundamental yet powerful form of artistic expression. In this paper, we introduce a novel task aimed at enhancing novice understanding of reproducibility in line drawings: reconstructing the stroke-bystroke drawing process from complex line art. This task poses substantial challenges, as it requires resolving stroke ambiguity, variations in stroke thickness, and stroke overlapping. To address these issues, we propose a hierarchical framework that emulates human drawing behavior, comprising three stages: (1) high-level generation of global semantic stroke order, (2) mid-level optimization of human drawing mechanics, and (3) low-level perceptual stroke rendering. Drawing inspiration from the human tendency to conceptualize the overall structure before refining local details, we first extract keyframes of the drawing sequence that guide global ordering using a diffusion-based model. Simultaneously, based on the assumption that humans can infer strokes from any cue point in a line drawing, we train a stroke renderer to extract variable-width substrokes at the pixel level. Lastly, we formulate a set of equations to model human drawing dynamics, enabling more detailed inference of stroke composition and sequencing within the identified keyframes. This framework effectively integrates high-level semantic understanding with low-level stroke reconstruction, facilitating stroke-level process recovery in complex line drawings. Extensive experiments and user studies demonstrate that our method produces relatively natural and coherent drawing process animations for high-quality line art. |  |\n\n1. Introduction  \nArtistic line drawing, or line art, is a fundamental form of visual expression in which monochromatic strokes — varying in thickness, curvature, and density — delineate contours, structures, and spatial relationships within a composition. As a highly abstracted representation of visual information, the creation of line art inherently reflects human cognitive strategies for decomposing complex subjects into hierarchical geometric primitives. This intrinsic connection positions the reconstruction of the line art drawing process as an interdisciplinary challenge at the intersection of computer graphics and the study of perceptual mechanisms in artistic cognition. In this paper, we focus on‘‘complex’’ line arts, defined as high-resolution images characterized by intricate stroke topology and a clean background, without the influence of real-world lighting or shading. A typical example is the line art found in digital illustrations. Despite their apparent simplicity, such drawings are difficult for novices to reproduce. Even when tasked with replicating a given drawing in this style, novices often struggle to determine where to begin, highlighting the need for tools that can reveal and guide the underlying drawing process.  \n∗ Corresponding author.  \nE-mail address: [zeyuwang@ust.hk](zeyuwang@ust.hk) (Z. Wang).  \nRecent advancements in artificial intelligence have shown promising results in reconstructing the drawing process for paintings from real-world images using stroke-based rendering approaches [1–4] or pixel-ba","cbCaipaIIJ4WHxiw","https://ap.wps.com/l/cbCaipaIIJ4WHxiw","pdf",7169702,11,"English","# Introduction\n## Problem motivation and challenges\n## Proposed framework: LineDrawer\n## Relation to prior work","[{\"question\":\"What is the main goal of LineDrawer?\",\"answer\":\"LineDrawer aims to reconstruct the stroke-by-stroke drawing process from complex line art, enhancing understanding of how such drawings can be reproduced.\"},{\"question\":\"What challenges does the method address in complex line art?\",\"answer\":\"It tackles stroke ambiguity, variations in stroke thickness, and overlapping strokes that create combinatorial ordering difficulties.\"},{\"question\":\"How does LineDrawer generate the reconstructed drawing sequence?\",\"answer\":\"It uses a hierarchical framework with three stages: global semantic stroke order from keyframes, mid-level optimization of drawing mechanics, and low-level perceptual stroke rendering for variable-width substrokes.\"}]","LineDrawer - Stroke-level process reconstruction of complex line art based on human perception | PDF",28]