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Reconstructing 3D characters from such sparse inputs has traditionally been challenging due to insufficient visual information. Recently, 3D generative models have enabled automatic reconstruction of plausible 3D characters by learning from large\u2010scale training data, but achieving reconstructions that accurately match the input model sheets remains limited. In this paper, we present a framework that leverages the power of a 3D generative model for initial reconstruction and enhances the output to faithfully reproduce input model sheets. For such faithful reconstruction, we must address two fundamental challenges: (1) the hand\u2010drawn nature inherently introduces multi\u2010view inconsistencies where the generated 3D geometry cannot perfectly align with all views, and (2) view\u2010dependent line elements along geometry boundaries interfere with accurate texture reconstruction. To address these challenges, we optimize the geometry to minimize multi\u2010view inconsistencies and introduce a deformable per\u2010pixel camera ray representation that resolves residual discrepancies in cross\u2010view correspondences. We also decompose drawings into three distinct layers of view\u2010dependent lines, view\u2010independent colors, and fine\u2010detail decals to separately handle view\u2010dependent and view\u2010independent components for consistent cross\u2010view reconstruction. Comprehensive experiments demonstrate that our method outperforms possible alternatives regardless of the choice of 3D generative model, while successfully preserving both artistic intent and visual fidelity of input model sheets.<\/jats:p>","DOI":"10.1111\/cgf.70323","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T13:15:10Z","timestamp":1775826910000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["3D Character Reconstruction from Hand\u2010drawn Model Sheets"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4423-8544","authenticated-orcid":false,"given":"Hyejeong","family":"Yoon","sequence":"first","affiliation":[{"name":"POSTECH  South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1442-9399","authenticated-orcid":false,"given":"Wonjong","family":"Jang","sequence":"additional","affiliation":[{"name":"POSTECH  South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5919-4184","authenticated-orcid":false,"given":"Yoonha","family":"Hwang","sequence":"additional","affiliation":[{"name":"POSTECH  South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8159-4271","authenticated-orcid":false,"given":"Seungyong","family":"Lee","sequence":"additional","affiliation":[{"name":"POSTECH  South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"e_1_2_10_2_2","doi-asserted-by":"crossref","unstructured":"Aigerman Noam Gupta Kunal Kim Vladimir G et al. \u201cNeural jacobian fields: Learning intrinsic mappings of arbitrary meshes\u201d.arXiv preprint arXiv:2205.02904(2022) 5.","DOI":"10.1145\/3528223.3530141"},{"issue":"4","key":"e_1_2_10_3_2","first-page":"106","article-title":"Patch-based optimization for image-based texture mapping","volume":"36","author":"Bi Sai","year":"2017","journal-title":"ACM Trans. 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