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[ICCV 2025] Official code of DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning
| Date | Stars |
|---|---|
| 2026-07-31 | 732 |
| 2026-08-02 | 732 |
| 2026-08-03 | 732 |
| 2026-08-04 | 733 |
| 2026-08-06 | 733 |
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<p align="center">
<h3 align="center"><strong>DeepMesh: Auto-Regressive Artist-Mesh Creation<br>With Reinforcement Learning</strong></h3>
<p align="center">
<a href="https://zhaorw02.github.io/">Ruowen Zhao</a><sup>1,2*</sup>,
<a href="https://jamesyjl.github.io/">Junliang Ye</a><sup>1,2*</sup>,
<a href="https://thuwzy.github.io/">Zhengyi Wang</a><sup>1,2*</sup>,<br>
<a href="">Guangce Liu</a><sup>2</sup>,
<a href="https://buaacyw.github.io/">Yiwen Chen</a><sup>3</sup>,
<a href="https://yikaiw.github.io/">Yikai Wang</a><sup>1</sup>,
<a href="https://ml.cs.tsinghua.edu.cn/~jun/index.shtml">Jun Zhu</a><sup>1,2†</sup>
<br>
<sup>*</sup>Equal Contribution.
<br>
<sup>†</sup>Corresponding authors.
<br>
<sup>1</sup>Tsinghua University,
<sup>2</sup>ShengShu,
<br>
<sup>3</sup>S-Lab, Nanyang Technological University,
</p>
<div align="center">
<a href='https://arxiv.org/abs/2503.15265'><img src='https://img.shields.io/badge/arXiv-2503.15265-b31b1b.svg'></a>
<a href='https://zhaorw02.github.io/DeepMesh/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
<a><img src='https://img.shields.io/badge/License-MIT-blue'></a>
<a href="https://huggingface.co/zzzrw/DeepMesh/tree/main"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Weights-HF-orange"></a>
<a href='https://www.youtube.com/watch?v=6grL7bSbQ2w'><img src='https://img.shields.io/badge/Youtube-Video-b31b1b.svg'>
</div>
<div align="center">
<img src="assets/teaser.png" alt="Demo" width="1024px" />
</div>
**All of the meshes above are generated by DeepMesh.** DeepMesh can generate high-quality meshes conditioned on the given point cloud by auto-regressive transformer.
## Release
- [3/20] 🔥🔥We released the pretrained weight of **DeepMesh** (0.5 B).
- [4/01] 🔥We optimized the inference code, achieving a 50% reduction in generation time.
## Contents
- [Release](#release)
- [Installation](#installation)
- [Usage](#usage)
- [Important Notes](#important-notes)
- [Todo](#todo)
- [Acknowledgement](#acknowledgement)
- [BibTeX](#bibtex)
## Installation
Our environment has been tested on Ubuntu 22, CUDA 11.8 with A100, A800 and A6000.
1. Clone our repo and create conda environment
```
git clone https://github.com/zhaorw02/DeepMesh.git && cd DeepMesh
conda env create -f environment.yaml
conda activate deepmesh
```
or you can create on CUDA 12.1.
```
conda create -n deepmesh python=3.12
conda activate deepmesh
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
pip3 install -U xformers --index-url https://download.pytorch.org/whl/cu121
git clone https://github.com/Dao-AILab/flash-attention
cd flash-attention
pip install packaging
python setup.py install
cd csrc/rotary && pip install .
cd ../layer_norm && pip install .
cd ../xentropy && pip install .
cd ../../.. && rm -r flash-attention
pip install trimesh beartype lightning safetensors open3d omegaconf sageattention triton scikit-image transformers
```
2. Install the pretrained model weight
```
pip install -U "huggingface_hub[cli]"
huggingface-cli login
huggingface-cli download zzzrw/DeepMesh --local-dir ./
```
## Usage
### Command line inference
```
# Note: if you want to use your own point cloud, please make sure the normal is included.
# Generate all obj/ply in your folder
CUDA_VISIBLE_DEVICES=0 torchrun --nproc-per-node=1 --master_port=12345 sample.py \
--model_path "your_model_path" \
--steps 90000 \
--input_path examples \
--output_path mesh_output \
--repeat_num 4 \
--temperature 0.5 \
# Generate the specified obj/ply in your folder
CUDA_VISIBLE_DEVICES=0 torchrun --nproc-per-node=1 --master_port=22345.py \
--model_path "your_model_path" \
--steps 90000 \
--input_path examples \
--output_path mesh_output \
--repeat_num 4 \
--uid_list Excerpt of 5,199 characters
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Zhengyi Wang · Tsinghua University · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
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