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Hi @aim-uofa 🤗
Niels here from the open-source team at Hugging Face. I discovered your work through Hugging Face's daily papers as yours got featured: https://huggingface.co/papers/2508.14811.
The paper page lets people discuss about your paper and lets them find artifacts about it (your models, datasets or demo for instance), you can also claim
the paper as yours which will show up on your public profile at HF, add Github and project page URLs.
I saw on your GitHub repository (https://github.com/aim-uofa/Tinker) in the "🚩 Plan" section that you are planning to release the "Data and Data Pipeline" and "Source code of Scene Completion Model". It'd be great to make these checkpoints for your Tinker model and the new multi-view editing dataset available on the 🤗 hub once they are ready, to improve their discoverability/visibility.
We can add tags so that people find them when filtering https://huggingface.co/models and https://huggingface.co/datasets.
Uploading models
For your Tinker model, which performs generalizable 3D editing, the relevant pipeline tag would likely be image-to-3d. See here for a guide: https://huggingface.co/docs/hub/models-uploading.
In this case, we could leverage the PyTorchModelHubMixin class which adds from_pretrained and push_to_hub to any custom nn.Module. Alternatively, one can leverages the hf_hub_download one-liner to download a checkpoint from the hub.
We encourage researchers to push each model checkpoint to a separate model repository, so that things like download stats also work. We can then also link the checkpoints to the paper page.
Uploading dataset
For your "first large-scale multi-view editing dataset," which is crucial for 3D editing, it would be awesome to make it available on 🤗. The relevant task category would be image-to-3d. People could then do:
from datasets import load_dataset
dataset = load_dataset("your-hf-org-or-username/your-dataset")See here for a guide: https://huggingface.co/docs/datasets/loading.
Besides that, there's the dataset viewer which allows people to quickly explore the first few rows of the data in the browser.
Let me know if you're interested/need any help regarding this!
Cheers,
Niels
ML Engineer @ HF 🤗