Update README.md

README.md

@@ -13,10 +13,73 @@ This repo is the official implementation of InstantMesh, a feed-forward framewor
 
 https://github.com/TencentARC/InstantMesh/assets/20635237/737bba2d-df45-4707-8557-1dd84f248764
 
+# ⚙️ Dependencies and Installation
 
-# Bibtex
+We recommand 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
 
-If you find our work useful for your research and applications, please cite using this BibTeX:
+# 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
+```
+
+# 💫 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).
+
+Please 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 running:
+```bash
+python app.py
+```
+
+## Running with command line
+
+To generate 3D meshes from images via command line, simply running:
+```bash
+python run.py configs/instant-mesh-large.yaml examples/ --save_video
+```
+
+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:
+```bash
+python run.py configs/instant-mesh-large.yaml examples/ --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/ --save_video --export_texmap
+```
+
+# 💻 Training
+
+We provide our training code to facilatate 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
+```
+
+# :books: Citation
+
+If you find our work useful for your research or applications, please cite using this BibTeX:
 
 ```BibTeX
 @article{xu2024instantmesh,
@@ -25,3 +88,13 @@ If you find our work useful for your research and applications, please cite usin
   journal={arXiv preprint},
   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/)