# GarmentImage: Raster Encoding of Garment Sewing Patterns with Diverse Topologies

arXiv: https://arxiv.org/abs/2505.02592 · HTML: https://mlsanity.com/paper/2505.02592/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2025.

## Built on by (most cited first, 11 of 11)

- [DressWild: Feed-Forward Pose-Agnostic Garment Sewing Pattern Generation from In-the-Wild Images](https://mlsanity.com/paper/2602.16502/) (2026)
- [Garment Particles: A 2D--3D Symmetric Garment Representation for Generation and Editing](https://mlsanity.com/paper/2605.26391/) (2026)
- [ReWeaver: Towards Simulation-Ready and Topology-Accurate Garment Reconstruction](https://mlsanity.com/paper/2601.16672/) (2026)
- [Learning Sewing Patterns via Latent Flow Matching of Implicit Fields](https://mlsanity.com/paper/2601.17740/) (2026)
- [AnySurf: Any Surface Generation with Directed Edge](https://mlsanity.com/paper/2605.26149/) (2026)
- [PatternGSL: A Structured Specification Language for Template-Free and Simulation-Ready 3D Garments](https://mlsanity.com/paper/2606.24564/) (2026)
- [Learning-based Seam Correspondence Reconstruction in Sewing Patterns](https://mlsanity.com/paper/2607.21213/) (2026)
- [SwiftTailor: Efficient 3D Garment Generation with Geometry Image Representation](https://mlsanity.com/paper/2603.19053/) (2026)
- [Stitched Embeddings: A Unified Latent Space for 3D Garments and 2D Patterns](https://mlsanity.com/paper/2607.00829/) (2026)
- [GarmentWeaver: Schema-Aware Structured Synthesis for Multimodal Sewing Patterns](https://mlsanity.com/paper/2608.30550/) (2026)
- [SewFusion: Tailored Generation of Topology and Panel-Level Geometry for Sewing Patterns](https://mlsanity.com/paper/2609.23548/) (2026)

## Abstract (arXiv)

Garment sewing patterns are the design language behind clothing, yet their current vector-based digital representations weren't built with machine learning in mind. Vector-based representation encodes a sewing pattern as a discrete set of panels, each defined as a sequence of lines and curves, stitching information between panels and the placement of each panel around a body. However, this representation causes two major challenges for neural networks: discontinuity in latent space between patterns with different topologies and limited generalization to garments with unseen topologies in the training data. In this work, we introduce GarmentImage, a unified raster-based sewing pattern representation. GarmentImage encodes a garment sewing pattern's geometry, topology and placement into multi-channel regular grids. Machine learning models trained on GarmentImage achieve seamless transitions between patterns with different topologies and show better generalization capabilities compared to models trained on vector-based representation. We demonstrate the effectiveness of GarmentImage across three applications: pattern exploration in latent space, text-based pattern editing, and image-to-pattern prediction. The results show that GarmentImage achieves superior performance on these applications using only simple convolutional networks.