mlsanityData to 7 Oct 2026

DeepIron: Predicting Unwarped Garment Texture from a Single Image

arXiv 2310.15447 ↗First posted 24 Oct 2023cs.GR cs.CVcited by 2

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Abstract (arXiv)

Realistic reconstruction of 3D clothing from an image has wide applications, such as avatar creation and virtual try-on. This paper presents a novel framework that reconstructs the texture map for 3D garments from a single image with pose. Assuming that 3D garments are modeled by stitching 2D garment sewing patterns, our specific goal is to generate a texture image for the sewing patterns. A key component of our framework, the Texture Unwarper, infers the original texture image from the input clothing image, which exhibits warping and occlusion of texture due to the user's body shape and pose. The Texture Unwarper effectively transforms between the input and output images by mapping the latent spaces of the two images. By inferring the unwarped original texture of the input garment, our method helps reconstruct 3D garment models that can show high-quality texture images realistically deformed for new poses. We validate the effectiveness of our approach through a comparison with other methods and ablation studies.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 33 of its 36 references that are in the map. First posted on arXiv 24 Oct 2023.

Builds on

33 of 36 references.

References · 33

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21 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024FabricDiffusion: High-Fidelity Texture Transfer for 3D Garments Generation from In-The-Wild Clothing Imagescited by 1
  2. 2026DressWild: Feed-Forward Pose-Agnostic Garment Sewing Pattern Generation from In-the-Wild Imagescited by 1