sinAshish / Argus-3D

Argus-3D: Pushing the Limits of 3D Shape Generation at Scale

Paper | Project Page

Installation

You can create an anaconda environment called argus-3d using

conda env create -f environment.yaml
conda activate argus-3d

Next, compile the extension modules. You can do this via

python setup.py build_ext --inplace

Generation

Download stage1 checkpoint and place it into output/PR256_ED512_EN8192.

Download stage2 checkpoint and place it into output/PR256_ED512_EN8192/class-guide/transformer3072_24_32.

Then you can try class-guide generation by run:

python generate_class-guide.py --batch_size 16 --cate chair

This script should create a folder output/PR256_ED512_EN8192/class-guide/transformer3072_24_32/class_cond where the output meshes are stored.

Note: Our model requires significant memory, and it's recommended to run it on a GPU with high VRAM capacity (40GB or above). Generating a single mesh on the A100 (80GB) takes approximately 50 seconds on average, while on V100 (32GB) it takes ~6 minutes.

Dataset

The occupancies, point clouds, and supplementary rendered images based on the Objaverse dataset can be downloaded from https://huggingface.co/datasets/BAAI/Objaverse-MIX

Coming Soon

• Image-guide generation
• Text-guide generation
• Training code

Shout-outs

Thanks to everyone who makes their code and models available.

• Convolutional Occupancy Networks
• Taming Transformers for High-Resolution Image Synthesis

Thanks for open-sourcing!

BibTeX

@misc{yu2023pushing,
      title={Pushing the Limits of 3D Shape Generation at Scale}, 
      author={Yu Wang and Xuelin Qian and Jingyang Huo and Tiejun Huang and Bo Zhao and Yanwei Fu},
      year={2023},
      eprint={2306.11510},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}