Texture Generation on 3D Meshes with Point-UV Diffusion

Project Page | Dataset | Paper

Texture Generation on 3D Meshes with Point-UV Diffusion (ICCV 2023 Oral)

Xin Yu, Peng Dai, Wenbo Li, Lan Ma, Zhengzhe Liu, Xiaojuan Qi

Table of Contents

• Introduction
• Installation
• Dataset Preparation
• Project Structure
• Configuration
• Training
• Testing
• Evaluation Metrics
• Citation
• Acknowledgments

Introduction

In this work, we delve into a novel texture representation based on UV maps and investigate the advanced diffusion model for texture generation. The 2D nature of the UV map enables it to circumvent the cost of high-resolution point/voxel representations. Besides, the UV map is compatible with arbitrary mesh topologies, thereby preserving the original geometric structures. We introduce Point-UV diffusion, a two stage coarse-to-fine framework consisting of point diffusion and UV diffusion. Specifically, we initially design a point diffusion model to generate color for sampled points that act as low-frequency texture components. This model is equipped with a style guidance mechanism that alleviates the impact of biased color distributions in the dataset and facilitates diversity during inference. Next, we project these colorized points onto the 2D UV space with 3D coordinate interpolation, thereby generating a coarse texture image that maintains 3D consistency and continuity. Given the coarse textured image, we develop a UV diffusion model with elaborately designed hybrid conditions to improve the quality of the textures.

Installation

1. Clone the Repository:
   Start by cloning the repository to your local machine.

2. Set Up the Environment:
   Create and activate a new conda environment named point_uv_diff with Python 3.8.15.

   conda create -n point_uv_diff python==3.8.15
   conda activate point_uv_diff

3. Install PyTorch and Related Packages:
   Install specific versions of PyTorch, torchvision, and torchaudio using the following command.

   pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113

4. Install PyTorch Geometric Dependencies:

   pip install --no-index --no-cache-dir torch-cluster torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-1.11.0+cu113.html 
   pip install --no-cache-dir torch-geometric

5. Install Additional Requirements:
   Finally, install the remaining dependencies listed in the requirements.txt file.

   pip install -r requirements.txt

Follow these steps to set up the development environment for the project.

Dataset Preparation

You can access our preprocessed dataset via this Dropbox link. After downloading and unzipping the dataset, the folder structure should be organized as follows:

.
├── clip_image_data 
│   ├── 03001627
│   └── 04379243
├── clip_text_data
│   ├── 03001627
│   └── 04379243
├── coarse_model
│   ├── 02828884
│   ├── 02958343
│   ├── 03001627
│   └── 04379243
├── final_split_files
│   ├── 02828884
│   ├── 02958343
│   ├── 03001627
│   └── 04379243
└── uv_model_512
    ├── 02828884
    ├── 02958343
    ├── 03001627
    └── 04379243

After successfully downloading the dataset, please remember to update the data_dir field in the configs/paths/default.yaml file to reflect the path where you've saved the dataset.

For example, open configs/paths/default.yaml and modify it as follows:

data_dir: 'path/to/your/downloaded/dataset'

We release the dataset processing scripts here.

Directory Descriptions

• clip_image_data: Contains the preprocessed data used for image-conditioned training and testing.
• clip_text_data: Contains the preprocessed data used for text-conditioned training and testing.
• coarse_model: Contains the preprocessed data used for coarse-stage training and testing.
• final_split_files: Contains the data split files for training and testing.
• uv_model_512: Contains the preprocessed data used for training and testing with a UV model at 512 resolution.

Project Structure

• src/: Contains the source code.
• configs/: Configuration files for training and testing.
• results/: Store the results here.

Configuration

The project uses YAML configuration files stored in the configs/ directory. You can specify settings for data modules, models, training, and evaluation.

Training

To train the model, navigate to the src/ directory and execute the following:

bash train.sh

The script uses the following command to launch the training:

python -m torch.distributed.launch --master_port [PORT] --nproc_per_node [NUM_GPUS] \
train.py experiment=[EXPERIMENT_NAME] \
datamodule.batch_size=[BATCH_SIZE_PER_GPU]

Testing

To test the model, navigate to the src/ directory and execute the following:

bash test.sh

The script uses the following command to launch the testing:

python -m torch.distributed.launch --master_port [PORT] --nproc_per_node [NUM_GPUS] \
test.py experiment=[EXPERIMENT_NAME] \
ckpt_name=[CHECKPOINT_PATH]

Downloading Pretrained Models

You can download our pretrained models via this Dropbox link. Please maintain the following directory structure; otherwise, you may encounter errors. For instance, for the image-conditioned model:

.
├── image_condition
│   ├── table_image_coarse
│   │   ├── ckpt
│   │   │    └── ckpt.pth

Evaluation Metrics

The evaluation metrics can be computed using the evaluate_metric.py script. For example, after doing cascaded inference for the table category and get the results, simply run:

python -m torch.distributed.launch --master_port 1234 --nproc_per_node 1 evaluate_metric.py \
experiment=table_fine \
+evaluate_result_folder=../results/test/pretrain/uncondition/table_fine/static_timestamped

Here evaluate_result_folder is the path to the generated results. The script will compute the FID and KID scores.

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{yu2023texture,
  title={Texture Generation on 3D Meshes with Point-UV Diffusion},
  author={Yu, Xin and Dai, Peng and Li, Wenbo and Ma, Lan and Liu, Zhengzhe and Qi, Xiaojuan},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={4206--4216},
  year={2023}
}

Acknowledgments

Our codebase is constructed upon frameworks such as Hydra and OmegaConf, which provide modularity and ease of modification.

Additionally, our implementation references several outstanding code repositories, specifically:

• PVD
• Guided Diffusion
• DALLE2-PyTorch
• NVDiffrast

We extend our gratitude to the developers of these libraries for making their code publicly available, thereby contributing to the broader research community.

Contact

If you have any questions, please contact Xin Yu via [email protected].