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View Selection for 3D Captioning via Diffusion Ranking

<a href="https://cap3d-um.github.io/"><img src="https://img.shields.io/static/v1?label=Project&message=Website&color=red" height=20.5></a> <a href="https://arxiv.org/abs/2404.07984"><img src="https://img.shields.io/badge/arXiv-2404.07984-b31b1b.svg" height=20.5></a> <a href="https://arxiv.org/abs/2306.07279"><img src="https://img.shields.io/badge/arXiv-2306.07279-b31b1b.svg" height=20.5></a>

Tiange Luo, Justin Johnson† Honglak Lee† (†Equal Advising)

Data download available at Hugging Face, including 1,002,422 3D-caption pairs covering the whole Objaverse and subset of Objaverse-XL datasets. We also the associated objects' point clouds and rendered images (with camera, depth, and MatAlpha information).

Overall Logistics

To identify key views of a 3D object for downstream tasks like captioning in DiffuRank, we follow a multi-step process:

• Rendering: We render the object from 28 distinct views using two different rendering settings, producing 2D images for each view.
• Captioning: We generate captions for all 28 images, resulting in 5 captions per image.
• Extracting: We extract ShapE latent codes, which serve as the 3D descriptor for the object.
• DiffuRank: Using DiffuRank, we calculate the alignment between the generated captions and the ShapE latent codes to assess their compatibility.
• GPT Captioning: Finally, the images whose captions show the highest alignment with the 3D descriptor are selected as the key views.

Rendering

Please first download our Blender via the below commands. You can use your own Blender, while may need to pip install several packages.

wget https://huggingface.co/datasets/tiange/Cap3D/resolve/main/misc/blender.zip
unzip blender.zip

Please run the below command to render objects into .png images saved at {parent_dir}/Cap3D_imgs/{uid}/{00000~00028}.png

# --object_path_pkl: point to a pickle file which store the object path
# --parent_dir: the directory store the rendered images and their associated camera matrix

# Rendered images will be stored at partent_dir/Cap3D_imgs/
# 8 views will be rendered for each object; camera placed horizontally around the object's default orientation
./blender-3.4.1-linux-x64/blender -b -P render_script_type1.py -- --object_path_pkl './example_material/example_object_path.pkl' --parent_dir './example_material'

# Rendered images will be stored at partent_dir/Cap3D_imgs/
# 20 views will be rendered for each object; camera placed randomly
./blender-3.4.1-linux-x64/blender -b -P render_script_type2.py -- --object_path_pkl './example_material/example_object_path.pkl' --parent_dir './example_material'

Captioning

We currently use BLIP2 to generate captions for rendered images. There are a lot of other new captioning model that can be used for this task.

pip install salesforce-lavis

# this program will scan all the folders inside partent_dir/Cap3D_imgs/
python caption_blip2.py --parent_dir ./example_material

Extract ShapE Latent Codes

Please go to shap-e folder to extract ShapE latent codes.

DiffuRank process

Please go to shap-e folder to perform DiffuRank.

Final Captioning with Key Views

Please use the below command. It will store the final captions as caption.csv.

python captioning_gpt.py --api_key 'YOUR_OPENAI_API_KEY'

Citation

If you find our code or data useful, please consider citing:

@article{luo2024view,
      title={View Selection for 3D Captioning via Diffusion Ranking},
      author={Luo, Tiange and Johnson, Justin and Lee, Honglak},
      journal={arXiv preprint arXiv:2404.07984},
      year={2024}
}

@article{luo2023scalable,
      title={Scalable 3D Captioning with Pretrained Models},
      author={Luo, Tiange and Rockwell, Chris and Lee, Honglak and Johnson, Justin},
      journal={arXiv preprint arXiv:2306.07279},
      year={2023}
}