IBRSteG: Learning a Generalizable Steganography Framework for 3D Gaussian Splatting
Paper โข 2606.30024 โข Published
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Check out the documentation for more information.
To keep this repository lightweight, all model checkpoints are hosted on Hugging Face.
Please download the required weights and place them in your local model_zoo/ directory.
| File | Purpose | Download |
|---|---|---|
gps_plus_final.pth |
Frozen GPS-Gaussian+ backbone checkpoint | Download |
ibrsteg_test_weight.pth |
Inference-only IBRSteG/GAS checkpoint for testing | Download |
Quick Download via CLI:
mkdir -p model_zoo
wget [https://huggingface.co/lingxiang2023/IBRSteG/resolve/main/model_zoo/gps_plus_final.pth](https://huggingface.co/lingxiang2023/IBRSteG/resolve/main/model_zoo/gps_plus_final.pth) -O model_zoo/gps_plus_final.pth
wget [https://huggingface.co/lingxiang2023/IBRSteG/resolve/main/model_zoo/ibrsteg_test_weight.pth](https://huggingface.co/lingxiang2023/IBRSteG/resolve/main/model_zoo/ibrsteg_test_weight.pth) -O model_zoo/ibrsteg_test_weight.pth
Note: The original full checkpoint (~489 MB, containing optimizer/scheduler states) has been trimmed to the inference-only ibrsteg_test_weight.pth (~163 MB, containing explicitly formulated camera parameter steganographic keys and minimal metadata) for faster evaluation.
If you find our work useful in your research, please consider citing our paper and starring the repository:
@article{kong2026ibrsteg,
title={IBRSteG: Learning a Generalizable Steganography Framework for 3D Gaussian Splatting},
author={Kong, Fanye and others},
journal={arXiv preprint arXiv:2606.30024},
year={2026}
}
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license: mit
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