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---
license: cc-by-nc-4.0
tags:
- normal-estimation
- depth-estimation
- diffusion
- transparent-objects
library_name: diffusers
pipeline_tag: image-to-image
---
# TransNormal
Surface normal estimation for transparent objects using diffusion models with DINOv3 semantic guidance.
## Usage
```python
from transnormal import TransNormalPipeline, create_dino_encoder
import torch
# Load DINO encoder (download separately)
dino_encoder = create_dino_encoder(
model_name="dinov3_vith16plus",
weights_path="path/to/dinov3_vith16plus",
projector_path="path/to/cross_attention_projector.pt",
device="cuda",
dtype=torch.bfloat16,
)
# Load pipeline
pipe = TransNormalPipeline.from_pretrained(
"longxiang-ai/transnormal-v1",
dino_encoder=dino_encoder,
torch_dtype=torch.bfloat16,
)
pipe = pipe.to("cuda")
# Inference
normal_map = pipe("image.jpg", output_type="pil")
```
## Citation
```bibtex
@article{transnormal2025,
title={TransNormal: Dense Visual Semantics for Diffusion-based Transparent Object Normal Estimation},
author={Li, Mingwei and Fan, Hehe and Yang, Yi},
year={2025}
}
```
## License
CC BY-NC 4.0
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