---
license: mit
task_categories:
- text-to-3d
- image-to-3d
---

# Garment Particles: A 2D–3D Symmetric Garment Representation for Generation and Editing

[Project Page](https://garment-particles.github.io) | [Paper](https://huggingface.co/papers/2605.26391) | [GitHub](https://github.com/garment-particles/GarmentParticles)

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](https://github.com/garment-particles/GarmentParticles), you can download and unpack the shards using the Hugging Face CLI:

```bash
# 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

```bibtex
@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}
}
```