| --- |
| 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. |
|
|
