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.

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. 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, which is not distributed under an open-source license and carries its own terms.

Citation

@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.