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https://github.com/xmu-xiaoma666/x-mesh

A pytorch implementation of “ X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance”
https://github.com/xmu-xiaoma666/x-mesh

Last synced: about 1 month ago
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A pytorch implementation of “ X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance”

Host: GitHub
URL: https://github.com/xmu-xiaoma666/x-mesh
Owner: xmu-xiaoma666
Created: 2023-03-27T07:44:53.000Z (over 1 year ago)
Default Branch: master
Last Pushed: 2024-01-12T02:54:17.000Z (12 months ago)
Last Synced: 2024-11-14T13:36:20.588Z (about 1 month ago)
Language: Python
Size: 71.5 MB
Stars: 27
Watchers: 4
Forks: 2
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

# X-Mesh

[【Paper】](https://arxiv.org/abs/2303.15764) [【Project Page】](https://xmu-xiaoma666.github.io/Projects/X-Mesh/)

A pytorch implementation of "X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance"

## Overview

## Getting Started
### Installation

**Note:** The below installation will fail if run on something other than a CUDA GPU machine.
```
conda env create --file xmesh.yml
conda activate xmesh
```
If you experience an error installing kaolin saying something like `nvcc not found`, you may need to set your `CUDA_HOME` environment variable to the 11.3 folder i.e. `export CUDA_HOME=/usr/local/cuda-11.3`, then rerunning the installation.

### System Requirements
- Python 3.9
- CUDA 11
- GPU w/ minimum 8 GB ram

### Dataset
The dataset has been placed in `./Dataset`.
There are two folders in this path:
- `mesh_MIT30` : this folder contains 30 kinds of meshes.
- `prompt_MIT30` : this folder contains annotated prompts for meshes.

### Run examples
#### Training One Mesh
```bash
python main.py --run branch --obj_path --output_dir --prompt "" --sigma 12.0 --clamp tanh --n_normaugs 4 --n_augs 1 --normmincrop 0.1 --normmaxcrop 0.4 --geoloss --colordepth 2 --normdepth 2 --frontview --frontview_std 4 --clipavg view --lr_decay 0.9 --clamp tanh --normclamp tanh --save_render --seed 23 --n_iter 1200 --normal_learning_rate 0.0005 --standardize --no_pe --symmetry --background 1 1 1

```

#### Training Code for MIT-30
Call the below shell scripts to generate styled results on the MIT-30 dataset.
```bash
# generate results for MIT-30
./script/train.sh

```
The outputs will be saved to `./output`, with the stylized .obj files, colored and uncolored render views, and screenshots during training.
#### Evaluation Code
**Test MSE**

First generate rendered images from 24 fixed angles, you can run following sctipt:
```bash
# '--data_path' is the saving path that you set during your training
# '--out_path' is the path that you want to save 24 rendered images

python generate_multiviews.py --data_path --out_path

```
Then you can calculate the MSE metric via running following script:
```bash
# '--out_path' is the path that you save 24 rendered images in the last step
python test_MSE.py --out_path

```

**Test ITS**

First, save all MSE for all iterations to a .json file:
```bash
# '--data_path' is the saving path that you set during your training
python save_similarity.py --data_path

```

Finally, we calculate the ITS metric:
```bash
# '--threshold' is the MSE threshold for ITS
python test_ITS.py --threshold

```

### Outputs