# xCloth: Extracting Template-free Textured 3D Clothes from a Monocular Image

arXiv: https://arxiv.org/abs/2208.12934 · HTML: https://mlsanity.com/paper/2208.12934/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 27 Aug 2022.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors](https://mlsanity.com/paper/2105.01859/) (2021)
- [Multi-Garment Net: Learning to Dress 3D People from Images](https://mlsanity.com/paper/1908.06903/) (2019)
- [Learning to Reconstruct People in Clothing from a Single RGB Camera](https://mlsanity.com/paper/1903.05885/) (2019)
- [Instance-level Human Parsing via Part Grouping Network](https://mlsanity.com/paper/1808.00157/) (2018)
- [BCNet: Learning Body and Cloth Shape from A Single Image](https://mlsanity.com/paper/2004.00214/) (2020)
- [Deep Fashion3D: A Dataset and Benchmark for 3D Garment Reconstruction from Single Images](https://mlsanity.com/paper/2003.12753/) (2020)
- [Continuous Surface Embeddings](https://mlsanity.com/paper/2011.12438/) (2020)
- [Learning to Transfer Texture from Clothing Images to 3D Humans](https://mlsanity.com/paper/2003.02050/) (2020)
- [Registering Explicit to Implicit: Towards High-Fidelity Garment mesh Reconstruction from Single Images](https://mlsanity.com/paper/2203.15007/) (2022)
- [Boundary First Flattening](https://mlsanity.com/paper/1704.06873/) (2017)
- [Robust 3D Garment Digitization from Monocular 2D Images for 3D Virtual Try-On Systems](https://mlsanity.com/paper/2111.15140/) (2021)
- [SHARP: Shape-Aware Reconstruction of People in Loose Clothing](https://mlsanity.com/paper/2205.11948/) (2022)

## Built on by (most cited first, 6 of 6)

- [Garment Recovery with Shape and Deformation Priors](https://mlsanity.com/paper/2311.10356/) (2023)
- [WordRobe: Text-Guided Generation of Textured 3D Garments](https://mlsanity.com/paper/2403.17541/) (2024)
- [Single View Garment Reconstruction Using Diffusion Mapping Via Pattern Coordinates](https://mlsanity.com/paper/2504.08353/) (2025)
- [AniDress: Animatable Loose-Dressed Avatar from Sparse Views Using Garment Rigging Model](https://mlsanity.com/paper/2401.15348/) (2024)
- [Dress-Me-Up: A Dataset & Method for Self-Supervised 3D Garment Retargeting](https://mlsanity.com/paper/2401.03108/) (2024)
- [From Air to Wear: Personalized 3D Digital Fashion with AR/VR Immersive 3D Sketching](https://mlsanity.com/paper/2505.09998/) (2025)

## Abstract (arXiv)

Existing approaches for 3D garment reconstruction either assume a predefined template for the garment geometry (restricting them to fixed clothing styles) or yield vertex colored meshes (lacking high-frequency textural details). Our novel framework co-learns geometric and semantic information of garment surface from the input monocular image for template-free textured 3D garment digitization. More specifically, we propose to extend PeeledHuman representation to predict the pixel-aligned, layered depth and semantic maps to extract 3D garments. The layered representation is further exploited to UV parametrize the arbitrary surface of the extracted garment without any human intervention to form a UV atlas. The texture is then imparted on the UV atlas in a hybrid fashion by first projecting pixels from the input image to UV space for the visible region, followed by inpainting the occluded regions. Thus, we are able to digitize arbitrarily loose clothing styles while retaining high-frequency textural details from a monocular image. We achieve high-fidelity 3D garment reconstruction results on three publicly available datasets and generalization on internet images.