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README.md

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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},
+}
+```