MeshAnything V2:
Artist-Created Mesh Generation
With Adjacent Mesh Tokenization

Yiwen Chen¹, Yikai Wang²*, Yihao Luo³, Zhengyi Wang²,
Zilong Chen², Jun Zhu², Chi Zhang⁴*, Guosheng Lin¹*
^*Corresponding authors.
¹Nanyang Technological University, ²Tsinghua University,
³Imperial College London, ⁴Westlake University

              

Contents

• Contents
• Installation
• Usage
• Important Notes
• Acknowledgement
• BibTeX

Installation

Our environment has been tested on Ubuntu 22, CUDA 11.8 with A800.

1. Clone our repo and create conda environment

git clone https://github.com/buaacyw/MeshAnythingV2.git && cd MeshAnythingV2
conda create -n MeshAnythingV2 python==3.10.13 -y
conda activate MeshAnythingV2
pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
pip install flash-attn --no-build-isolation

Usage

Local Gradio Demo

python app.py

Mesh Command line inference

# folder input
python main.py --input_dir examples --out_dir mesh_output --input_type mesh

# single file input
python main.py --input_path examples/wand.obj --out_dir mesh_output --input_type mesh

# Preprocess with Marching Cubes first
python main.py --input_dir examples --out_dir mesh_output --input_type mesh --mc

Point Cloud Command line inference

# Note: if you want to use your own point cloud, please make sure the normal is included.
# The file format should be a .npy file with shape (N, 6), where N is the number of points. The first 3 columns are the coordinates, and the last 3 columns are the normal.

# inference for folder
python main.py --input_dir pc_examples --out_dir pc_output --input_type pc_normal

# inference for single file
python main.py --input_path pc_examples/mouse.npy --out_dir pc_output --input_type pc_normal

Important Notes

• It takes about 10GB and 60s to generate a mesh on an A800 GPU.
• The input mesh will be normalized to a unit bounding box. The up vector of the input mesh should be +Y for better results.
• Limited by computational resources, MeshAnything is trained on meshes with fewer than 1600 faces and cannot generate meshes with more than 1600 faces. The shape of the input mesh should be sharp enough; otherwise, it will be challenging to represent it with only 1600 faces. Thus, feed-forward 3D generation methods may often produce bad results due to insufficient shape quality. We suggest using results from 3D reconstruction, scanning, SDS-based method (like DreamCraft3D) or Rodin as the input of MeshAnything.
• Please refer to https://huggingface.co/spaces/Yiwen-ntu/MeshAnything/tree/main/examples for more examples.

Acknowledgement

Our code is based on these wonderful repos:

• MeshAnything
• MeshGPT
• meshgpt-pytorch
• Michelangelo
• transformers
• vector-quantize-pytorch

BibTeX

@misc{chen2024meshanything,
  title={MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers},
  author={Yiwen Chen and Tong He and Di Huang and Weicai Ye and Sijin Chen and Jiaxiang Tang and Xin Chen and Zhongang Cai and Lei Yang and Gang Yu and Guosheng Lin and Chi Zhang},
  year={2024},
  eprint={2406.10163},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}