Hi @AlbanGauthier 🤗
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work on Arxiv and noticed it got featured on the Hugging Face paper page: https://huggingface.co/papers/2512.13950.
The paper page allows people to discuss your paper and find related artifacts (such as models and datasets). You can also claim the paper as yours, which will show up on your public profile at HF, and add GitHub and project page URLs.
I saw in your GitHub repository (https://github.com/graphdeco-inria/svbrdf-evaluation) that you have released "UNet-HF checkpoints" for SVBRDF prediction. Would you be interested in hosting these pre-trained models on https://huggingface.co/models?
Hosting them on Hugging Face would significantly enhance their visibility and discoverability. We can add relevant tags in the model cards, link them directly to your paper page, and make it easier for others to find and use your work.
If you're interested, here's a guide on uploading models: https://huggingface.co/docs/hub/models-uploading. For custom PyTorch models, the PyTorchModelHubMixin class (https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) can be very useful as it adds from_pretrained and push_to_hub functionalities. Alternatively, users can download models directly using hf_hub_download (https://huggingface.co/docs/huggingface_hub/en/guides/download#download-a-single-file).
Once uploaded, we can also link these models to your paper page (read here: https://huggingface.co/docs/hub/en/model-cards#linking-a-paper) to further improve discoverability.
You could also consider building a demo for your models on Spaces! We can even provide a ZeroGPU grant, giving you A100 GPUs for free.
Please let me know if you're interested or need any guidance.
Kind regards,
Niels
Hi @AlbanGauthier 🤗
I'm Niels and work as part of the open-source team at Hugging Face. I discovered your work on Arxiv and noticed it got featured on the Hugging Face paper page: https://huggingface.co/papers/2512.13950.
The paper page allows people to discuss your paper and find related artifacts (such as models and datasets). You can also claim the paper as yours, which will show up on your public profile at HF, and add GitHub and project page URLs.
I saw in your GitHub repository (
https://github.com/graphdeco-inria/svbrdf-evaluation) that you have released "UNet-HF checkpoints" for SVBRDF prediction. Would you be interested in hosting these pre-trained models onhttps://huggingface.co/models?Hosting them on Hugging Face would significantly enhance their visibility and discoverability. We can add relevant tags in the model cards, link them directly to your paper page, and make it easier for others to find and use your work.
If you're interested, here's a guide on uploading models:
https://huggingface.co/docs/hub/models-uploading. For custom PyTorch models, thePyTorchModelHubMixinclass (https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) can be very useful as it addsfrom_pretrainedandpush_to_hubfunctionalities. Alternatively, users can download models directly usinghf_hub_download(https://huggingface.co/docs/huggingface_hub/en/guides/download#download-a-single-file).Once uploaded, we can also link these models to your paper page (read here:
https://huggingface.co/docs/hub/en/model-cards#linking-a-paper) to further improve discoverability.You could also consider building a demo for your models on Spaces! We can even provide a ZeroGPU grant, giving you A100 GPUs for free.
Please let me know if you're interested or need any guidance.
Kind regards,
Niels