MTN (Multi-Scale Triplane Network)

This repository contains the official implementation of Progressive Text-to-3D Generation for Automatic 3D Prototyping ( https://arxiv.org/abs/2309.14600).

Paper

Video results

https://github.com/Texaser/MTN/assets/50570271/bdc776a6-ee2d-43ff-9ee3-21784799d3cb

https://github.com/Texaser/MTN/assets/50570271/197fa808-154b-4671-8446-8350b1e166d6

For more videos, please refer to https://www.youtube.com/watch?v=LH6-wKg30FQ

Instructions:

Install the requirements:

pip install -r requirements.txt

To use DeepFloyd-IF, you need to accept the usage conditions from hugging face, and login with huggingface-cli login in command line.

Start training!

# choose stable-diffusion version python main.py --text "a hamburger" --workspace trial -O --sd_version 2.1 # use DeepFloyd-IF for guidance: python main.py --text "a hamburger" --workspace trial -O --IF python main.py --text "a hamburger" --workspace trial -O --IF --vram_O # requires ~24G GPU memory python main.py -O --text "a tiger cub" --workspace trial_perpneg_if_tiger --iters 6000 --IF --batch_size 1 --perpneg python main.py -O --text "a shiba dog wearing sunglasses" --workspace trial_perpneg_if_shiba --iters 6000 --IF --batch_size 1 --perpneg python main.py -O --text "a octopus toy" --workspace trial_perpneg_if_octopus --iters 6000 --IF --batch_size 1 --perpneg # larger absolute value of negative_w is used for the following command because the defult negative weight of -2 is not enough to make the diffusion model to produce the views as desired python main.py -O --text "a shiba dog wearing sunglasses" --workspace trial_perpneg_if_shiba --iters 6000 --IF --batch_size 1 --perpneg --negative_w -3.0 # after the training is finished: # test (exporting 360 degree video) python main.py --workspace trial -O --test # also save a mesh (with obj, mtl, and png texture) python main.py --workspace trial -O --test --save_mesh # test with a GUI (free view control!) python main.py --workspace trial -O --test --gui

Tested environments

torch 1.13 & CUDA 11.5 on a V100.

Citation

If you find this work useful, a citation will be appreciated via:

@article{yi2023progressive, title={Progressive Text-to-3D Generation for Automatic 3D Prototyping}, author={Yi, Han and Zheng, Zhedong and Xu, Xiangyu and Chua, Tat-seng}, journal={arXiv preprint arXiv:2309.14600}, year={2023} }