MeshAnythingV2

--- license: mit pipeline_tag: image-to-3d tags: - model_hub_mixin - pytorch_model_hub_mixin --- # MeshAnythingV2 Library: [https://github.com/buaacyw/MeshAnythingV2](https://github.com/buaacyw/MeshAnythingV2) ## Contents - [Contents](#contents) - [Installation](#installation) - [Usage](#usage) - [Important Notes](#important-notes) - [Acknowledgement](#acknowledgement) - [BibTeX](#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 pip install -U gradio ``` ## Usage ### For text/image to Artist-Create Mesh. We suggest using [Rodin](https://hyperhuman.deemos.com/rodin) to first achieve text or image to dense mesh. And then input the dense mesh to us. ``` # Put the output obj file of Rodin to rodin_result and using the following command to generate the Artist-Created Mesh. # We suggest using the --mc flag to preprocess the input mesh with Marching Cubes first. This helps us to align the inference point cloud to our training domain. python main.py --input_dir rodin_result --out_dir mesh_output --input_type mesh --mc ``` ### Local Gradio Demo <a href='https://github.com/gradio-app/gradio'><img src='https://img.shields.io/github/stars/gradio-app/gradio'></a> ``` python app.py ``` ### Mesh Command line inference #### Important Notes: If your mesh input is not produced by Marching Cubes, We suggest you to preprocess the mesh with Marching Cubes first (simply by adding --mc). ``` # 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 # The mc resolution is default to be 128. For some delicate mesh, this resolution is not sufficient. Raise this resolution takes more time to preprocess but should achieve a better result. # Change it by : --mc_level 7 -> 128 (2^7), --mc...

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