# 3D Clothed Human Reconstruction in the Wild

arXiv: https://arxiv.org/abs/2207.10053 · HTML: https://mlsanity.com/paper/2207.10053/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 35 references that are in the map.
- First posted on arXiv 20 Jul 2022.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [ICON: Implicit Clothed humans Obtained from Normals](https://mlsanity.com/paper/2112.09127/) (2021)
- [ARCH: Animatable Reconstruction of Clothed Humans](https://mlsanity.com/paper/2004.04572/) (2020)
- [Multi-Garment Net: Learning to Dress 3D People from Images](https://mlsanity.com/paper/1908.06903/) (2019)
- [DeepHuman: 3D Human Reconstruction from a Single Image](https://mlsanity.com/paper/1903.06473/) (2019)
- [Self-Correction for Human Parsing](https://mlsanity.com/paper/1910.09777/) (2019)
- [ARCH++: Animation-Ready Clothed Human Reconstruction Revisited](https://mlsanity.com/paper/2108.07845/) (2021)
- [Learning to Reconstruct People in Clothing from a Single RGB Camera](https://mlsanity.com/paper/1903.05885/) (2019)
- [Tex2Shape: Detailed Full Human Body Geometry From a Single Image](https://mlsanity.com/paper/1904.08645/) (2019)
- [SMPLicit: Topology-aware Generative Model for Clothed People](https://mlsanity.com/paper/2103.06871/) (2021)
- [TailorNet: Predicting Clothing in 3D as a Function of Human Pose, Shape and Garment Style](https://mlsanity.com/paper/2003.04583/) (2020)
- [BodyNet: Volumetric Inference of 3D Human Body Shapes](https://mlsanity.com/paper/1804.04875/) (2018)
- [DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images](https://mlsanity.com/paper/1901.07973/) (2019)
- [Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing](https://mlsanity.com/paper/2204.08906/) (2022)
- [BCNet: Learning Body and Cloth Shape from A Single Image](https://mlsanity.com/paper/2004.00214/) (2020)
- [CLOTH3D: Clothed 3D Humans](https://mlsanity.com/paper/1912.02792/) (2019)
- [Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction](https://mlsanity.com/paper/2006.08072/) (2020)
- [Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing](https://mlsanity.com/paper/1703.05446/) (2017)
- [Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation](https://mlsanity.com/paper/2011.11534/) (2020)
- [3D Human Body Reconstruction from a Single Image via Volumetric Regression](https://mlsanity.com/paper/1809.03770/) (2018)

## Built on by (most cited first, 30 of 42)

- [ECON: Explicit Clothed humans Optimized via Normal integration](https://mlsanity.com/paper/2212.07422/) (2022)
- [GaussianAvatar: Towards Realistic Human Avatar Modeling from a Single Video via Animatable 3D Gaussians](https://mlsanity.com/paper/2312.02134/) (2023)
- [Vid2Avatar: 3D Avatar Reconstruction from Videos in the Wild via Self-supervised Scene Decomposition](https://mlsanity.com/paper/2302.11566/) (2023)
- [4D-DRESS: A 4D Dataset of Real-world Human Clothing with Semantic Annotations](https://mlsanity.com/paper/2404.18630/) (2024)
- [DrapeNet: Garment Generation and Self-Supervised Draping](https://mlsanity.com/paper/2211.11277/) (2022)
- [Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification](https://mlsanity.com/paper/2308.10658/) (2023)
- [Towards Garment Sewing Pattern Reconstruction from a Single Image](https://mlsanity.com/paper/2311.04218/) (2023)
- [ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns](https://mlsanity.com/paper/2305.14100/) (2023)
- [REC-MV: REconstructing 3D Dynamic Cloth from Monocular Videos](https://mlsanity.com/paper/2305.14236/) (2023)
- [Joint Reconstruction of 3D Human and Object via Contact-Based Refinement Transformer](https://mlsanity.com/paper/2404.04819/) (2024)
- [Garment3DGen: 3D Garment Stylization and Texture Generation](https://mlsanity.com/paper/2403.18816/) (2024)
- [Garment Recovery with Shape and Deformation Priors](https://mlsanity.com/paper/2311.10356/) (2023)
- [ReLoo: Reconstructing Humans Dressed in Loose Garments from Monocular Video in the Wild](https://mlsanity.com/paper/2409.15269/) (2024)
- [HiLo: Detailed and Robust 3D Clothed Human Reconstruction with High-and Low-Frequency Information of Parametric Models](https://mlsanity.com/paper/2404.04876/) (2024)
- [Single View Garment Reconstruction Using Diffusion Mapping Via Pattern Coordinates](https://mlsanity.com/paper/2504.08353/) (2025)
- [Human Image Generation: A Comprehensive Survey](https://mlsanity.com/paper/2212.08896/) (2022)
- [Three Recipes for Better 3D Pseudo-GTs of 3D Human Mesh Estimation in the Wild](https://mlsanity.com/paper/2304.04875/) (2023)
- [GarmentDiffusion: 3D Garment Sewing Pattern Generation with Multimodal Diffusion Transformers](https://mlsanity.com/paper/2504.21476/) (2025)
- [WonderHuman: Hallucinating Unseen Parts in Dynamic 3D Human Reconstruction](https://mlsanity.com/paper/2502.01045/) (2025)
- [GarmentCrafter: Progressive Novel View Synthesis for Single-View 3D Garment Reconstruction and Editing](https://mlsanity.com/paper/2503.08678/) (2025)
- [High-Quality Animatable Dynamic Garment Reconstruction from Monocular Videos](https://mlsanity.com/paper/2311.01214/) (2023)
- [Design2Cloth: 3D Cloth Generation from 2D Masks](https://mlsanity.com/paper/2404.02686/) (2024)
- [DeClotH: Decomposable 3D Cloth and Human Body Reconstruction from a Single Image](https://mlsanity.com/paper/2503.19373/) (2025)
- [R3-Avatar: Record and Retrieve Temporal Codebook for Reconstructing Photorealistic Human Avatars](https://mlsanity.com/paper/2503.12751/) (2025)
- [Image2Garment: Simulation-ready Garment Generation from a Single Image](https://mlsanity.com/paper/2601.09658/) (2026)
- [DeepIron: Predicting Unwarped Garment Texture from a Single Image](https://mlsanity.com/paper/2310.15447/) (2023)
- [DLCA-Recon: Dynamic Loose Clothing Avatar Reconstruction from Monocular Videos](https://mlsanity.com/paper/2312.12096/) (2023)
- [An Embeddable Implicit IUVD Representation for Part-based 3D Human Surface Reconstruction](https://mlsanity.com/paper/2401.16810/) (2024)
- [PARTE: Part-Guided Texturing for 3D Human Reconstruction from a Single Image](https://mlsanity.com/paper/2507.17332/) (2025)
- [NGD: Neural Gradient Based Deformation for Monocular Garment Reconstruction](https://mlsanity.com/paper/2508.17712/) (2025)

## Abstract (arXiv)

Although much progress has been made in 3D clothed human reconstruction, most of the existing methods fail to produce robust results from in-the-wild images, which contain diverse human poses and appearances. This is mainly due to the large domain gap between training datasets and in-the-wild datasets. The training datasets are usually synthetic ones, which contain rendered images from GT 3D scans. However, such datasets contain simple human poses and less natural image appearances compared to those of real in-the-wild datasets, which makes generalization of it to in-the-wild images extremely challenging. To resolve this issue, in this work, we propose ClothWild, a 3D clothed human reconstruction framework that firstly addresses the robustness on in-thewild images. First, for the robustness to the domain gap, we propose a weakly supervised pipeline that is trainable with 2D supervision targets of in-the-wild datasets. Second, we design a DensePose-based loss function to reduce ambiguities of the weak supervision. Extensive empirical tests on several public in-the-wild datasets demonstrate that our proposed ClothWild produces much more accurate and robust results than the state-of-the-art methods. The codes are available in here: https://github.com/hygenie1228/ClothWild_RELEASE.