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Official implementation of "MicroDreamer: Zero-shot 3D Generation in ~20 Seconds by Score-based Iterative Reconstruction".

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MicroDreamer

Official implementation of MicroDreamer: Zero-shot 3D Generation in ~20 Seconds by Score-based Iterative Reconstruction.

display.mp4

News

[10/2024] Add a new mesh export method from LGM

Installation

The codebase is built on DreamGaussian. For installation,

conda create -n MicroDreamer python=3.11
conda activate MicroDreamer

pip install -r requirements.txt

# a modified gaussian splatting (+ depth, alpha rendering)
git clone --recursive https://github.com/ashawkey/diff-gaussian-rasterization
pip install ./diff-gaussian-rasterization

# The commit hash we used
# d986da0d4cf2dfeb43b9a379b6e9fa0a7f3f7eea

# simple-knn
pip install ./simple-knn

# nvdiffrast
pip install git+https://github.com/NVlabs/nvdiffrast/

# The version we used
# pip install git+https://github.com/NVlabs/nvdiffrast/@0.3.1

# kiuikit
pip install git+https://github.com/ashawkey/kiuikit/

# The version we used
# pip install git+https://github.com/ashawkey/kiuikit/@0.2.3

# To use ImageDream, also install:
pip install git+https://github.com/bytedance/ImageDream/#subdirectory=extern/ImageDream

# The commit hash we used
# 26c3972e586f0c8d2f6c6b297aa9d792d06abebb

Usage

Image-to-3D:

### preprocess
# background removal and recentering, save rgba at 256x256
python process.py test_data/name.jpg

# save at a larger resolution
python process.py test_data/name.jpg --size 512

# process all jpg images under a dir
python process.py test_data

### training gaussian stage
# train 20 iters and export ckpt & coarse_mesh to logs
python main.py --config configs/image_sai.yaml input=test_data/name_rgba.png save_path=name_rgba

### training mesh stage
# auto load coarse_mesh and refine 3 iters, export fine_mesh to logs
python main2.py --config configs/image_sai.yaml input=test_data/name_rgba.png save_path=name_rgba

Image+Text-to-3D (ImageDream):

### training gaussian stage
python main.py --config configs/imagedream.yaml input=test_data/ghost_rgba.png prompt="a ghost eating hamburger" save_path=ghost_rgba

Calculate for CLIP similarity:

PYTHONPATH='.' python scripts/cal_sim.py

More Results

total_1.mp4
total_2.mp4

Acknowledgement

This work is built on many amazing open source projects, thanks to all the authors!

BibTeX

@misc{chen2024microdreamerzeroshot3dgeneration,
      title={MicroDreamer: Zero-shot 3D Generation in $\sim$20 Seconds by Score-based Iterative Reconstruction}, 
      author={Luxi Chen and Zhengyi Wang and Zihan Zhou and Tingting Gao and Hang Su and Jun Zhu and Chongxuan Li},
      year={2024},
      eprint={2404.19525},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2404.19525}, 
}

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Official implementation of "MicroDreamer: Zero-shot 3D Generation in ~20 Seconds by Score-based Iterative Reconstruction".

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