# Garment4D: Garment Reconstruction from Point Cloud Sequences

arXiv: https://arxiv.org/abs/2112.04159 · HTML: https://mlsanity.com/paper/2112.04159/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 33 references that are in the map.
- First posted on arXiv 8 Dec 2021.

## Builds on (most cited first, 28 of 28)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [Multi-Garment Net: Learning to Dress 3D People from Images](https://mlsanity.com/paper/1908.06903/) (2019)
- [Video Based Reconstruction of 3D People Models](https://mlsanity.com/paper/1803.04758/) (2018)
- [SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes](https://mlsanity.com/paper/2104.03953/) (2021)
- [DeepHuman: 3D Human Reconstruction from a Single Image](https://mlsanity.com/paper/1903.06473/) (2019)
- [SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks](https://mlsanity.com/paper/2104.03313/) (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)
- [DeepCap: Monocular Human Performance Capture Using Weak Supervision](https://mlsanity.com/paper/2003.08325/) (2020)
- [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)
- [Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction](https://mlsanity.com/paper/2007.11432/) (2020)
- [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)
- [Detailed Human Avatars from Monocular Video](https://mlsanity.com/paper/1808.01338/) (2018)
- [Learning-Based Animation of Clothing for Virtual Try-On](https://mlsanity.com/paper/1903.07190/) (2019)
- [Detailed Human Shape Estimation from a Single Image by Hierarchical Mesh Deformation](https://mlsanity.com/paper/1904.10506/) (2019)
- [SiCloPe: Silhouette-Based Clothed People](https://mlsanity.com/paper/1901.00049/) (2018)
- [Moulding Humans: Non-parametric 3D Human Shape Estimation from Single Images](https://mlsanity.com/paper/1908.00439/) (2019)
- [MonoClothCap: Towards Temporally Coherent Clothing Capture from Monocular RGB Video](https://mlsanity.com/paper/2009.10711/) (2020)
- [DeepCloth: Neural Garment Representation for Shape and Style Editing](https://mlsanity.com/paper/2011.14619/) (2020)
- [FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second](https://mlsanity.com/paper/1909.00883/) (2019)
- [3D Human Body Reconstruction from a Single Image via Volumetric Regression](https://mlsanity.com/paper/1809.03770/) (2018)
- [A Neural Network for Detailed Human Depth Estimation from a Single Image](https://mlsanity.com/paper/1910.01275/) (2019)

## Built on by (most cited first, 23 of 23)

- [SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation](https://mlsanity.com/paper/2309.17448/) (2023)
- [HuMMan: Multi-Modal 4D Human Dataset for Versatile Sensing and Modeling](https://mlsanity.com/paper/2204.13686/) (2022)
- [EVA3D: Compositional 3D Human Generation from 2D Image Collections](https://mlsanity.com/paper/2210.04888/) (2022)
- [AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars](https://mlsanity.com/paper/2205.08535/) (2022)
- [Learning Disentangled Avatars with Hybrid 3D Representations](https://mlsanity.com/paper/2309.06441/) (2023)
- [Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulation](https://mlsanity.com/paper/2206.02881/) (2022)
- [Versatile Multi-Modal Pre-Training for Human-Centric Perception](https://mlsanity.com/paper/2203.13815/) (2022)
- [FLAG3D: A 3D Fitness Activity Dataset with Language Instruction](https://mlsanity.com/paper/2212.04638/) (2022)
- [Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing](https://mlsanity.com/paper/2206.15470/) (2022)
- [Structure-Preserving 3D Garment Modeling with Neural Sewing Machines](https://mlsanity.com/paper/2211.06701/) (2022)
- [GarmentTracking: Category-Level Garment Pose Tracking](https://mlsanity.com/paper/2303.13913/) (2023)
- [Reconstruction of Manipulated Garment with Guided Deformation Prior](https://mlsanity.com/paper/2405.10934/) (2024)
- [SPnet: Estimating Garment Sewing Patterns from a Single Image](https://mlsanity.com/paper/2312.16264/) (2023)
- [A Generative Multi-Resolution Pyramid and Normal-Conditioning 3D Cloth Draping](https://mlsanity.com/paper/2311.02700/) (2023)
- [USR: Unsupervised Separated 3D Garment and Human Reconstruction via Geometry and Semantic Consistency](https://mlsanity.com/paper/2302.10518/) (2023)
- [Cloth2Body: Generating 3D Human Body Mesh from 2D Clothing](https://mlsanity.com/paper/2309.16189/) (2023)
- [MonoCloth: Reconstruction and Animation of Cloth-Decoupled Human Avatars from Monocular Videos](https://mlsanity.com/paper/2508.04505/) (2025)
- [NGL: Natural Garment Language for Training-Free Sewing Pattern Estimation](https://mlsanity.com/paper/2602.20700/) (2026)
- [Unveiling Hidden Vulnerabilities in Digital Human Generation via Adversarial Attacks](https://mlsanity.com/paper/2504.17457/) (2025)
- [Point cloud segmentation for 3D Clothed Human Layering](https://mlsanity.com/paper/2508.05531/) (2025)
- [LatentStealth: Unnoticeable and Efficient Adversarial Attacks on Expressive Human Pose and Shape Estimation](https://mlsanity.com/paper/2505.12009/) (2025)
- [From Mannequin to Human: A Pose-Aware and Identity-Preserving Video Generation Framework for Lifelike Clothing Display](https://mlsanity.com/paper/2510.16833/) (2025)
- [Chatting about Upper-Body Expressive Human Pose and Shape Estimation](https://mlsanity.com/paper/2604.17959/) (2026)

## Abstract (arXiv)

Learning to reconstruct 3D garments is important for dressing 3D human bodies of different shapes in different poses. Previous works typically rely on 2D images as input, which however suffer from the scale and pose ambiguities. To circumvent the problems caused by 2D images, we propose a principled framework, Garment4D, that uses 3D point cloud sequences of dressed humans for garment reconstruction. Garment4D has three dedicated steps: sequential garments registration, canonical garment estimation, and posed garment reconstruction. The main challenges are two-fold: 1) effective 3D feature learning for fine details, and 2) capture of garment dynamics caused by the interaction between garments and the human body, especially for loose garments like skirts. To unravel these problems, we introduce a novel Proposal-Guided Hierarchical Feature Network and Iterative Graph Convolution Network, which integrate both high-level semantic features and low-level geometric features for fine details reconstruction. Furthermore, we propose a Temporal Transformer for smooth garment motions capture. Unlike non-parametric methods, the reconstructed garment meshes by our method are separable from the human body and have strong interpretability, which is desirable for downstream tasks. As the first attempt at this task, high-quality reconstruction results are qualitatively and quantitatively illustrated through extensive experiments. Codes are available at https://github.com/hongfz16/Garment4D.