[从文本到 3D] 输入文本描述，生成 3D Mesh

[layumi]

小红书的 Demo： https://www.xiaohongshu.com/explore/651ae551000000001e00c7b0

Youtube 的 Demo： https://www.youtube.com/watch?v=wxoOcO-9NWU

代码在 https://github.com/Texaser/MTN

欢迎大家关注！ Star ！感谢各位大佬！

输入一个文本，大概训练 1 个小时，就可以产生对应的 3D model 了。

相比之前的算法，由于我们采用 progressive 的形式，在形态上的鲁棒性更强，收敛速度也快一些。

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:

1. 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.

1. 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}
}

Acknowledgement

This code base is built upon the following awesome open-source projects: Stable DreamFusion, threestudio

Thanks the authors for their remarkable job !

[lj394139]

又是一篇顶会