---
license: other
license_name: adpncl-1.0
license_link: LICENSE
tags:
  - 3d
  - mesh-generation
  - flow-matching
  - topology
pipeline_tag: image-to-3d
---

# TriFlow — Pretrained Weights

Pretrained checkpoints for **TriFlow: Generating Artist-Like 3D Mesh Topology via
Nearest-Vertex Vector Fields** (ECCV 2026).

TriFlow generates compact 3D meshes with artist-like triangle topology from input
geometry conditions such as signed distance fields. Mesh topology is represented as a
**Nearest-Vertex Vector Field (NVF)** over the surface; a latent flow-matching model
synthesises this field, and a constrained **Quadric Error Metric (QEM)** simplification
extracts the final mesh.

## Files

| File | Size | Stage |
|---|---|---|
| `sdf_vae.safetensors` | 152 MB | Stage 1 — SDF VAE |
| `nvv_vae.safetensors` | 412 MB | Stage 2 — NVF VAE |
| `flow_model.safetensors` | 755 MB | Stage 3 — Latent Flow Matching |

## Usage

These weights are used by the TriFlow codebase — see the repository for setup,
data preparation, training and inference instructions.

```bash
hf download lihcxr/TriFlow \
    flow_model.safetensors sdf_vae.safetensors nvv_vae.safetensors \
    --local-dir checkpoints
```

`inference.py` loads them from `checkpoints/` by default.

## License

Released under the **Automotive Development Public Non-Commercial License v1.0**
(ADPNCL) — see [`LICENSE`](LICENSE). This license permits **non-commercial use only**;
please read it in full before use.

Note that TriFlow's mesh-processing pipeline additionally depends on
[MeshLib](https://github.com/MeshInspector/MeshLib), which is not distributed under an
open-source license and carries its own terms.

## Citation

```bibtex
@inproceedings{li2026triflow,
  title = {TriFlow: Generating Artist-Like 3D Mesh Topology via Nearest-Vertex Vector Fields},
  author = {Li, Haoxuan and Erko{\c{c}}, Ziya and Sirigatti, Daniele and Rosov, Vladislav and Li, Lei and Dai, Angela and Nie{\ss}ner, Matthias},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year = {2026},
}
```

This work was funded by AUDI AG.