InstantMesh

This repo is the official implementation of InstantMesh, a feed-forward framework for efficient 3D mesh generation from a single image based on the LRM/Instant3D architecture.

• 🔥🔥 Release Zero123++ fine-tuning code.
• 🔥🔥 Support for running gradio demo on two GPUs to save memory.
• 🔥🔥 Support for running demo with docker. Please refer to the docker directory.
• Release inference and training code.
• Release model weights.
• Release huggingface gradio demo. Please try it at demo link.
• Add support for more multi-view diffusion models.

We recommend using Python>=3.10, PyTorch>=2.1.0, and CUDA>=12.1.

conda create --name instantmesh python=3.10
conda activate instantmesh
pip install -U pip

# Ensure Ninja is installed
conda install Ninja

# Install the correct version of CUDA
conda install cuda -c nvidia/label/cuda-12.1.0

# Install PyTorch and xformers
# You may need to install another xformers version if you use a different PyTorch version
pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu121
pip install xformers==0.0.22.post7

# Install other requirements
pip install -r requirements.txt

We provide 4 sparse-view reconstruction model variants and a customized Zero123++ UNet for white-background image generation in the model card.

Our inference script will download the models automatically. Alternatively, you can manually download the models and put them under the ckpts/ directory.

By default, we use the instant-mesh-large reconstruction model variant.

To start a gradio demo in your local machine, simply run:

python app.py

If you have multiple GPUs in your machine, the demo app will run on two GPUs automatically to save memory. You can also force it to run on a single GPU:

CUDA_VISIBLE_DEVICES=0 python app.py

Alternatively, you can run the demo with docker. Please follow the instructions in the docker directory.

To generate 3D meshes from images via command line, simply run:

python run.py configs/instant-mesh-large.yaml examples/hatsune_miku.png --save_video

We use rembg to segment the foreground object. If the input image already has an alpha mask, please specify the no_rembg flag:

python run.py configs/instant-mesh-large.yaml examples/hatsune_miku.png --save_video --no_rembg

By default, our script exports a .obj mesh with vertex colors, please specify the --export_texmap flag if you hope to export a mesh with a texture map instead (this will cost longer time):

python run.py configs/instant-mesh-large.yaml examples/hatsune_miku.png --save_video --export_texmap

Please use a different .yaml config file in the configs directory if you hope to use other reconstruction model variants. For example, using the instant-nerf-large model for generation:

python run.py configs/instant-nerf-large.yaml examples/hatsune_miku.png --save_video

Note: When using the NeRF model variants for image-to-3D generation, exporting a mesh with texture map by specifying --export_texmap may cost long time in the UV unwarping step since the default iso-surface extraction resolution is 256. You can set a lower iso-surface extraction resolution in the config file.

We provide our training code to facilitate future research. But we cannot provide the training dataset due to its size. Please refer to our dataloader for more details.

To train the sparse-view reconstruction models, please run:

# Training on NeRF representation
python train.py --base configs/instant-nerf-large-train.yaml --gpus 0,1,2,3,4,5,6,7 --num_nodes 1

# Training on Mesh representation
python train.py --base configs/instant-mesh-large-train.yaml --gpus 0,1,2,3,4,5,6,7 --num_nodes 1

We also provide our Zero123++ fine-tuning code since it is frequently requested. The running command is:

python train.py --base configs/zero123plus-finetune.yaml --gpus 0,1,2,3,4,5,6,7 --num_nodes 1

If you find our work useful for your research or applications, please cite using this BibTeX:

@article{xu2024instantmesh,
  title={InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models},
  author={Xu, Jiale and Cheng, Weihao and Gao, Yiming and Wang, Xintao and Gao, Shenghua and Shan, Ying},
  journal={arXiv preprint arXiv:2404.07191},
  year={2024}
}

We thank the authors of the following projects for their excellent contributions to 3D generative AI!

• Zero123++
• OpenLRM
• FlexiCubes
• Instant3D

Thank @camenduru for implementing Replicate Demo and Colab Demo!
Thank @jtydhr88 for implementing ComfyUI support!