Instructions to use tencent/Hunyuan3D-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Hunyuan3D-2
How to use tencent/Hunyuan3D-2 with Hunyuan3D-2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Results for Hunyuan3D on living organisms (blind human votes, with reference photos)
I run Taxon3D, a blind pairwise arena where people compare two anonymised 3D models of a named
organism, with CC-licensed photos of the real thing beside them. Three Hunyuan3D checkpoints are
on the image-to-3D board. Hunyuan3D v3 scores 1044, 95% interval 1001 to 1089, over 112
head-to-head games, which puts it in the second tier of 12 models. v2 and 3.1 are also ranked.
Every head-to-head record is here:
https://taxon3d.org/models/fal:hunyuan3d-v3?c=hf-hunyuan
Every task ships the input photo, so you can see exactly what the model was given. The set is
plants, fungi and animals, which are harder than props: thin surfaces, branching, self-occlusion.
Two things that might be useful here: the failure modes are visible per task, so you can see
which organisms each checkpoint struggles with, and having three versions on the same tasks makes
the version-to-version change measurable rather than anecdotal. Code is MIT and the votes are
published on Hugging Face.
Happy to answer questions about the protocol.