# Learning a Shared Shape Space for Multimodal Garment Design

arXiv: https://arxiv.org/abs/1806.11335 · HTML: https://mlsanity.com/paper/1806.11335/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2018.

## Built on by (most cited first, 30 of 52)

- [BEDLAM: A Synthetic Dataset of Bodies Exhibiting Detailed Lifelike Animated Motion](https://mlsanity.com/paper/2306.16940/) (2023)
- [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)
- [Self-Supervised Collision Handling via Generative 3D Garment Models for Virtual Try-On](https://mlsanity.com/paper/2105.06462/) (2021)
- [FS-COCO: Towards Understanding of Freehand Sketches of Common Objects in Context](https://mlsanity.com/paper/2203.02113/) (2022)
- [DressCode: Autoregressively Sewing and Generating Garments from Text Guidance](https://mlsanity.com/paper/2401.16465/) (2024)
- [DrapeNet: Garment Generation and Self-Supervised Draping](https://mlsanity.com/paper/2211.11277/) (2022)
- [Generating Datasets of 3D Garments with Sewing Patterns](https://mlsanity.com/paper/2109.05633/) (2021)
- [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)
- [GarmentCodeData: A Dataset of 3D Made-to-Measure Garments With Sewing Patterns](https://mlsanity.com/paper/2405.17609/) (2024)
- [Dynamic Neural Garments](https://mlsanity.com/paper/2102.11811/) (2021)
- [Garment3DGen: 3D Garment Stylization and Texture Generation](https://mlsanity.com/paper/2403.18816/) (2024)
- [ChatGarment: Garment Estimation, Generation and Editing via Large Language Models](https://mlsanity.com/paper/2412.17811/) (2024)
- [CloSe: A 3D Clothing Segmentation Dataset and Model](https://mlsanity.com/paper/2401.12051/) (2024)
- [GaussianVTON: 3D Human Virtual Try-ON via Multi-Stage Gaussian Splatting Editing with Image Prompting](https://mlsanity.com/paper/2405.07472/) (2024)
- [Dress-1-to-3: Single Image to Simulation-Ready 3D Outfit with Diffusion Prior and Differentiable Physics](https://mlsanity.com/paper/2502.03449/) (2025)
- [Motion Guided Deep Dynamic 3D Garments](https://mlsanity.com/paper/2209.11449/) (2022)
- [Structure-Preserving 3D Garment Modeling with Neural Sewing Machines](https://mlsanity.com/paper/2211.06701/) (2022)
- [Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapes](https://mlsanity.com/paper/2312.04043/) (2023)
- [Multimodal Latent Diffusion Model for Complex Sewing Pattern Generation](https://mlsanity.com/paper/2412.14453/) (2024)
- [NeuralTailor: Reconstructing Sewing Pattern Structures from 3D Point Clouds of Garments](https://mlsanity.com/paper/2201.13063/) (2022)
- [ClotheDreamer: Text-Guided Garment Generation with 3D Gaussians](https://mlsanity.com/paper/2406.16815/) (2024)
- [GarmentDiffusion: 3D Garment Sewing Pattern Generation with Multimodal Diffusion Transformers](https://mlsanity.com/paper/2504.21476/) (2025)
- [Neural Garment Dynamics via Manifold-Aware Transformers](https://mlsanity.com/paper/2407.06101/) (2024)
- [A Repulsive Force Unit for Garment Collision Handling in Neural Networks](https://mlsanity.com/paper/2207.13871/) (2022)
- [ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Layered Clothes](https://mlsanity.com/paper/2304.03492/) (2023)
- [Cloth2Tex: A Customized Cloth Texture Generation Pipeline for 3D Virtual Try-On](https://mlsanity.com/paper/2308.04288/) (2023)

## Abstract (arXiv)

Designing real and virtual garments is becoming extremely demanding with rapidly changing fashion trends and increasing need for synthesizing realistic dressed digital humans for various applications. This necessitates creating simple and effective workflows to facilitate authoring sewing patterns customized to garment and target body shapes to achieve desired looks. Traditional workflow involves a trial-and-error procedure wherein a mannequin is draped to judge the resultant folds and the sewing pattern iteratively adjusted until the desired look is achieved. This requires time and experience. Instead, we present a data-driven approach wherein the user directly indicates desired fold patterns simply by sketching while our system estimates corresponding garment and body shape parameters at interactive rates. The recovered parameters can then be further edited and the updated draped garment previewed. Technically, we achieve this via a novel shared shape space that allows the user to seamlessly specify desired characteristics across multimodal input em without requiring to run garment simulation at design time. We evaluate our approach qualitatively via a user study and quantitatively against test datasets, and demonstrate how our system can generate a rich quality of on-body garments targeted for a range of body shapes while achieving desired fold characteristics.