Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/TencentARC/InstantMesh
InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models
https://github.com/TencentARC/InstantMesh
Last synced: 24 days ago
JSON representation
InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models
- Host: GitHub
- URL: https://github.com/TencentARC/InstantMesh
- Owner: TencentARC
- License: apache-2.0
- Created: 2024-04-10T11:22:38.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-04-18T03:02:32.000Z (8 months ago)
- Last Synced: 2024-04-18T10:11:21.177Z (8 months ago)
- Language: Python
- Size: 31.1 MB
- Stars: 810
- Watchers: 20
- Forks: 45
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - TencentARC/InstantMesh
README
# InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models
---
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.
https://github.com/TencentARC/InstantMesh/assets/20635237/dab3511e-e7c6-4c0b-bab7-15772045c47d
# 🚩 Features and Todo List
- [x] 🔥🔥 Release Zero123++ fine-tuning code.
- [x] 🔥🔥 Support for running gradio demo on two GPUs to save memory.
- [x] 🔥🔥 Support for running demo with docker. Please refer to the [docker](docker/) directory.
- [x] Release inference and training code.
- [x] Release model weights.
- [x] Release huggingface gradio demo. Please try it at [demo](https://huggingface.co/spaces/TencentARC/InstantMesh) link.
- [ ] Add support for more multi-view diffusion models.
# ⚙️ Dependencies and Installation
We recommend using `Python>=3.10`, `PyTorch>=2.1.0`, and `CUDA>=12.1`.
```bash
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 Triton
pip install triton
# Install other requirements
pip install -r requirements.txt
```
# 💫 How to Use
## Download the models
We provide 4 sparse-view reconstruction model variants and a customized Zero123++ UNet for white-background image generation in the [model card](https://huggingface.co/TencentARC/InstantMesh).
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.
## Start a local gradio demo
To start a gradio demo in your local machine, simply run:
```bash
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:
```bash
CUDA_VISIBLE_DEVICES=0 python app.py
```
Alternatively, you can run the demo with docker. Please follow the instructions in the [docker](docker/) directory.
## Running with command line
To generate 3D meshes from images via command line, simply run:
```bash
python run.py configs/instant-mesh-large.yaml examples/hatsune_miku.png --save_video
```
We use [rembg](https://github.com/danielgatis/rembg) to segment the foreground object. If the input image already has an alpha mask, please specify the `no_rembg` flag:
```bash
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):
```bash
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](./configs) directory if you hope to use other reconstruction model variants. For example, using the `instant-nerf-large` model for generation:
```bash
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.
# 💻 Training
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](src/data/objaverse.py) for more details.
To train the sparse-view reconstruction models, please run:
```bash
# 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:
```bash
python train.py --base configs/zero123plus-finetune.yaml --gpus 0,1,2,3,4,5,6,7 --num_nodes 1
```
# :books: Citation
If you find our work useful for your research or applications, please cite using this BibTeX:
```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}
}
```
# 🤗 Acknowledgements
We thank the authors of the following projects for their excellent contributions to 3D generative AI!
- [Zero123++](https://github.com/SUDO-AI-3D/zero123plus)
- [OpenLRM](https://github.com/3DTopia/OpenLRM)
- [FlexiCubes](https://github.com/nv-tlabs/FlexiCubes)
- [Instant3D](https://instant-3d.github.io/)
Thank [@camenduru](https://github.com/camenduru) for implementing [Replicate Demo](https://replicate.com/camenduru/instantmesh) and [Colab Demo](https://colab.research.google.com/github/camenduru/InstantMesh-jupyter/blob/main/InstantMesh_jupyter.ipynb)!
Thank [@jtydhr88](https://github.com/jtydhr88) for implementing [ComfyUI support](https://github.com/jtydhr88/ComfyUI-InstantMesh)!