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        <identifier>oai:figshare.com:article/33944528</identifier>
        <datestamp>2026-09-20T06:14:49Z</datestamp>
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          <dc:title>From footwear lasts to generative AI: a CAD-driven framework for geometry-conditioned technical sketching</dc:title>
          <dc:creator>Juan José Hinojo-Pérez (25077200)</dc:creator>
          <dc:creator>Vicente Manuel López-Martín (25077203)</dc:creator>
          <dc:creator>David Sánchez-Pastor (25077206)</dc:creator>
          <dc:creator>Júlia Valdés-Martínez (25077209)</dc:creator>
          <dc:creator>Carlos Javier Bonete-Andréu (25077212)</dc:creator>
          <dc:creator>José Francisco Gómez-Hernández (25077215)</dc:creator>
          <dc:subject>Biophysics</dc:subject>
          <dc:subject>Genetics</dc:subject>
          <dc:subject>Biotechnology</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Science Policy</dc:subject>
          <dc:subject>Generative AI</dc:subject>
          <dc:subject>footwear design</dc:subject>
          <dc:subject>footwear lasts</dc:subject>
          <dc:subject>computer-aided design (CAD)</dc:subject>
          <dc:subject>geometry-conditioned technical sketching</dc:subject>
          <dc:description>&lt;p&gt;Generative AI supports creative design, but footwear sketches must match real-last geometry and transfer to CAD-based pattern-making workflows. Existing footwear datasets mainly cover product images, side-view sketches, retrieval, classification, or virtual try-on, without explicitly relating last geometry to technical design lines. This paper proposes a CAD-driven methodological framework for geometry-conditioned technical footwear design and presents ShoeLastSketch, its proprietary dataset-level operationalisation. Each sample pairs a line-free flattened-last input image with a target image containing CAD-derived sketch lines on the same geometry. The resulting dataset includes multilabel semantic annotations in professional footwear terminology and is generated from master models, valid design combinations, and last-to-last transfer procedures. With five core footwear typologies, 75 lasts, and 37,310 paired samples, ShoeLastSketch provides a geometry-conditioned, semantically controlled, CAD-traceable representation of technical footwear design. Downstream experiments empirically demonstrate the dataset’s utility: its structured representation supports learnable attribute recognition and geometry-conditioned sketch generation while preserving predefined technical constraints.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-20T06:14:49Z</dc:date>
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          <dc:identifier>10.6084/m9.figshare.33944528.v1</dc:identifier>
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          <dc:rights>CC BY 4.0</dc:rights>
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