# DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation

**[Wang Zhao¹](https://thuzhaowang.github.io), [Yan-Pei Cao²](https://yanpei.me/), [Jiale Xu¹](https://bluestyle97.github.io/), [Yuejiang Dong^1,3](https://scholar.google.com.hk/citations?user=0i7bPj8AAAAJ&hl=zh-CN), [Ying Shan¹](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en)** ¹ARC Lab, Tencent PCG ²VAST ³Tsinghua University

--- ## 🚩 Overview This repository contains code release for our technical report "DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation". ## ⚙️ Installation First clone this repository with [Infinigen](https://github.com/princeton-vl/infinigen) as the submodule: ``` git clone -r https://github.com/TencentARC/DI-PCG.git cd DI-PCG git submodule update --init --recursive ``` We recommend using anaconda to install the dependencies: ``` conda create -n di-pcg python=3.10.14 conda activate di-pcg conda install pytorch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 pytorch-cuda=11.8 -c pytorch -c nvidia pip install -r requirements.txt ``` ## 🚀 Usage For a quick start, try the huggingface gradio demo [here](https://huggingface.co/spaces/TencentARC/DI-PCG). ### Download models We provide the pretrained diffusion models for chair, vase, table, basket, flower and dandelion. You can download them from [model card]() and put them in `./pretrained_models/`. Alternatively, the inference script will automatically download the pretrained models for you. ### Local gradio demo To run the gradio demo locally, run: ``` python app.py ``` ### Inference To run the inference demo, simply use: ``` python ./scripts/sample_diffusion.py --config ./configs/demo/chair_demo.yaml ``` This script processes all the chair images in the `./examples/chair` folder and saves the generated 3D models and their rendered images in `./logs`. To generate other categories, use the corresponding YAML config file such as `vase_demo.yaml`. Currently we supprt `chair`, `table`, `vase`, `basket`, `flower` and `dandelion` generators developped by [Infinigen](https://github.com/princeton-vl/infinigen). ``` python ./scripts/sample_diffusion.py --config ./configs/demo/vase_demo.yaml ``` ### Training We train a diffusion model for each procedural generator. The training data is generated by randomly sampling the PCG and render multi-view images. To prepare the training data, run: ``` python ./scripts/prepare_data.py --generator ChairFactory --save_root /path/to/save/training/data ``` Replace `ChairFactory` with other category options as detailed in the `./scripts/prepare_data.py` file. This script also conducts offline augmentation and saves the extracted DINOv2 features for each image, which may consume a lot of disk storage. You can adjust the number of the generated data and the render configurations accordingly. After generating the training data, start the training by: ``` python ./scripts/train_diffusion.py --config ./configs/train/chair_train.yaml ``` ### Use your own PCG DI-PCG is general for any procedural generator. To train a diffusion model for your PCG, you need to implement the `get_params_dict`, `update_params`, `spawn_assets`, `finalize_assets` functions and place your PCG in `./core/assets/`. Also change the `num_params` in your training YAML config file. If you have any question, feel free to open an issue or contact us. ## Citation If you find our work useful for your research or applications, please cite using this BibTeX: ```BibTeX @article{zhao2024dipcg, title={DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation}, author={Zhao, Wang and Cao, Yanpei and Xu, Jiale and Dong, Yuejiang and Shan, Ying}, journal={arXiv preprint arxiv:2412.15200}, year={2024} } ``` ## 🤗 Acknowledgements DI-PCG is built on top of some awesome open-source projects: [Infinigen](https://github.com/princeton-vl/infinigen), [Fast-DiT](https://github.com/chuanyangjin/fast-DiT). We sincerely thank them all.