metadata
license: mit
task_categories:
- text-to-3d
- image-to-3d
Garment Particles: A 2D–3D Symmetric Garment Representation for Generation and Editing
Project Page | Paper | GitHub
Garment Particles is a 5D point-cloud representation that jointly encodes 2D sewing patterns and 3D geometry. This representation enables Garment Particles Flow (GPF), a framework that supports intuitive garment generation from high-level inputs such as text, images, and sketches, as well as various editing operations.
Dataset Structure
The dataset consists of:
data/particles-*.tar: 26 shards of per-garment particle data (rand_<id>/garment_particles_rand_<id>.h5+stats.txt).splits/garment_particle_v2_{train,test}_11182025.txt: Train and test split files.
Usage
As described in the official repository, you can download and unpack the shards using the Hugging Face CLI:
# Download the shards
huggingface-cli download georgeNakayama/GarmentParticles --repo-type dataset --local-dir garment_data
# Unpack the shards
cd garment_data
for t in data/*.tar; do tar -xf "$t"; done
cd ..
This will extract the data into a structure like garment_data/rand_<id>/garment_particles_rand_<id>.h5.
Citation
@article{garmentparticles2026,
title={Garment Particles: A 2D--3D Symmetric Garment Representation for Generation and Editing},
author={Nakayama, George and others},
journal={SIGGRAPH Conference Papers},
year={2026}
}