mlsanityData to 7 Oct 2026

GarmentZoom: Generating Zoomable Images from Garment Listings

arXiv 2606.29535 ↗First posted 28 Jun 2026cs.CVcited by 1

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Abstract (arXiv)

Online product listings for garments often include an overview photo and a close-up to show garment details. However, each photo focuses on either field of view or garment detail, forcing users to alternate between views and breaking browsing continuity. We present GarmentZoom, a system that enhances the full-view photo to match the fidelity of its accompanying close-up, enabling seamless zoom-and-pan exploration. Unlike standard reference-based super-resolution, our setting involves close-up references that are spatially unaligned with the full view, and scale factors that vary substantially across garments 3-20×. Prior work typically relies on alignment to transfer details or requires per-instance fine-tuning to memorize them. Instead, we train a single model that supports a continuous range of scales across diverse garments. Our approach synthesizes details without requiring spatial alignment and matches the quality of per-instance methods with a fraction of the training cost.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 28 references that are in the map. First posted on arXiv 28 Jun 2026.

Builds on

20 of 28 references.

References · 20

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8 more
  1. 2017Learning Texture Manifolds with the Periodic Spatial GANcited by 53
  2. 2022Reference-based Image Super-Resolution with Deformable Attention Transformercited by 50
  3. 2017TextureGAN: Controlling Deep Image Synthesis with Texture Patchescited by 47
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  8. 2024FabricDiffusion: High-Fidelity Texture Transfer for 3D Garments Generation from In-The-Wild Clothing Imagescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026GraftSR: Grafting Authentic Textures for Real-World Image Super-Resolution via Identical-Instance Guidancecited by 0