# Fully Convolutional Graph Neural Networks for Parametric Virtual Try-On

arXiv: https://arxiv.org/abs/2009.04592 · HTML: https://mlsanity.com/paper/2009.04592/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2020.

## Built on by (most cited first, 30 of 48)

- [SMPLicit: Topology-aware Generative Model for Clothed People](https://mlsanity.com/paper/2103.06871/) (2021)
- [Neural-GIF: Neural Generalized Implicit Functions for Animating People in Clothing](https://mlsanity.com/paper/2108.08807/) (2021)
- [The Power of Points for Modeling Humans in Clothing](https://mlsanity.com/paper/2109.01137/) (2021)
- [SNUG: Self-Supervised Neural Dynamic Garments](https://mlsanity.com/paper/2204.02219/) (2022)
- [Capturing and Animation of Body and Clothing from Monocular Video](https://mlsanity.com/paper/2210.01868/) (2022)
- [Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On](https://mlsanity.com/paper/2105.06462/) (2021)
- [Modeling Clothing as a Separate Layer for an Animatable Human Avatar](https://mlsanity.com/paper/2106.14879/) (2021)
- [PhysAvatar: Learning the Physics of Dressed 3D Avatars from Visual Observations](https://mlsanity.com/paper/2404.04421/) (2024)
- [Learning Implicit Templates for Point-Based Clothed Human Modeling](https://mlsanity.com/paper/2207.06955/) (2022)
- [Learning Disentangled Avatars with Hybrid 3D Representations](https://mlsanity.com/paper/2309.06441/) (2023)
- [DrapeNet: Garment Generation and Self-Supervised Draping](https://mlsanity.com/paper/2211.11277/) (2022)
- [Predicting Loose-Fitting Garment Deformations Using Bone-Driven Motion Networks](https://mlsanity.com/paper/2205.01355/) (2022)
- [Generating Datasets of 3D Garments with Sewing Patterns](https://mlsanity.com/paper/2109.05633/) (2021)
- [ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns](https://mlsanity.com/paper/2305.14100/) (2023)
- [Neural Point-based Shape Modeling of Humans in Challenging Clothing](https://mlsanity.com/paper/2209.06814/) (2022)
- [GarmentCodeData: A Dataset of 3D Made-to-Measure Garments With Sewing Patterns](https://mlsanity.com/paper/2405.17609/) (2024)
- [PERGAMO: Personalized 3D Garments from Monocular Video](https://mlsanity.com/paper/2210.15040/) (2022)
- [CloSET: Modeling Clothed Humans on Continuous Surface with Explicit Template Decomposition](https://mlsanity.com/paper/2304.03167/) (2023)
- [GALA: Generating Animatable Layered Assets from a Single Scan](https://mlsanity.com/paper/2401.12979/) (2024)
- [AvatarFusion: Zero-shot Generation of Clothing-Decoupled 3D Avatars Using 2D Diffusion](https://mlsanity.com/paper/2307.06526/) (2023)
- [CaPhy: Capturing Physical Properties for Animatable Human Avatars](https://mlsanity.com/paper/2308.05925/) (2023)
- [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)
- [NCHO: Unsupervised Learning for Neural 3D Composition of Humans and Objects](https://mlsanity.com/paper/2305.14345/) (2023)
- [Disco4D: Disentangled 4D Human Generation and Animation from a Single Image](https://mlsanity.com/paper/2409.17280/) (2024)
- [Towards Multi-Layered 3D Garments Animation](https://mlsanity.com/paper/2305.10418/) (2023)
- [Efficient 3D Articulated Human Generation with Layered Surface Volumes](https://mlsanity.com/paper/2307.05462/) (2023)
- [Shape Controllable Virtual Try-on for Underwear Models](https://mlsanity.com/paper/2107.13156/) (2021)
- [PICA: Physics-Integrated Clothed Avatar](https://mlsanity.com/paper/2407.05324/) (2024)
- [SPnet: Estimating Garment Sewing Patterns from a Single Image](https://mlsanity.com/paper/2312.16264/) (2023)

## Abstract (arXiv)

We present a learning-based approach for virtual try-on applications based on a fully convolutional graph neural network. In contrast to existing data-driven models, which are trained for a specific garment or mesh topology, our fully convolutional model can cope with a large family of garments, represented as parametric predefined 2D panels with arbitrary mesh topology, including long dresses, shirts, and tight tops. Under the hood, our novel geometric deep learning approach learns to drape 3D garments by decoupling the three different sources of deformations that condition the fit of clothing: garment type, target body shape, and material. Specifically, we first learn a regressor that predicts the 3D drape of the input parametric garment when worn by a mean body shape. Then, after a mesh topology optimization step where we generate a sufficient level of detail for the input garment type, we further deform the mesh to reproduce deformations caused by the target body shape. Finally, we predict fine-scale details such as wrinkles that depend mostly on the garment material. We qualitatively and quantitatively demonstrate that our fully convolutional approach outperforms existing methods in terms of generalization capabilities and memory requirements, and therefore it opens the door to more general learning-based models for virtual try-on applications.