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license: apache-2.0
tags:
- text-to-video
- image-to-video
- neural-materials
- wan
- siggraph
---
# VideoNeuMat — weights
Model weights for **VideoNeuMat: Neural Material Extraction from Generative Video Models** (SIGGRAPH 2026).
- Project page: https://bowenxueai.github.io/VideoNeuMat/
- Code: https://github.com/bowenxueai/videoneumatcode
The pipeline turns a **text prompt (or image)** into an 81-frame 1024×1024 material video with a
fine-tuned Wan-2.1 video model, then a **feed-forward LRM** extracts a re-renderable neural material
(BRDF + displacement) from that video.
## Files
| path | what | base model |
|---|---|---|
| `wan14b/step-10000.safetensors` | **T2V** material generator — full fine-tuned DiT (27 GB) | Wan2.1-T2V-14B |
| `wan14b_t2v_lora/step-9000.safetensors` | **T2V** material generator — LoRA (293 MB) | Wan2.1-T2V-14B |
| `wan14b_i2v/step-9500.safetensors` | **I2V** material generator — full fine-tuned DiT (31 GB) | Wan2.1-I2V-14B |
| `lrm/latent_module.pth` | **LRM** encoder — material → latent (feed-forward) | — |
| `lrm/mlp.pth` | shared neural-material **MLP** decoder | — |
All three generators produce the same 81-pose sparse-rig material video that the LRM consumes.
The base Wan-2.1 weights (`Wan-AI/Wan2.1-T2V-14B`, `Wan-AI/Wan2.1-I2V-14B`) are downloaded separately
from the official Wan-AI repos — see the code repo's README and `release/download_weights.sh`.
The generator weights are fine-tunes of Wan-2.1 (Apache-2.0). The VideoNeuMat code is MIT-licensed.
## Citation
```bibtex
@inproceedings{xue2026videoneumat,
author = {Xue, Bowen and Hadadan, Saeed and Zeng, Zheng and Rousselle, Fabrice and Montazeri, Zahra and Hasan, Milos},
title = {VideoNeuMat: Neural Material Extraction from Generative Video Models},
booktitle = {ACM SIGGRAPH 2026 Conference Papers},
year = {2026},
}
```
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