Add model card

README.md

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+---
+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.