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CityDreamer: Compositional Generative Model of Unbounded 3D Cities

Haozhe Xie, Zhaoxi Chen, Fangzhou Hong, Ziwei Liu

S-Lab, Nanyang Technological University

Changelog 🔥

• [2024/06/10] The training code is released.
• [2024/03/28] The testing code is released.
• [2024/03/03] The hugging face demo is available.
• [2024/02/27] The OSM and GoogleEarth datasets is released.
• [2023/08/15] The repo is created.

Cite this work 📝

@inproceedings{xie2024citydreamer,
  title     = {City{D}reamer: Compositional Generative Model of Unbounded 3{D} Cities},
  author    = {Xie, Haozhe and 
               Chen, Zhaoxi and 
               Hong, Fangzhou and 
               Liu, Ziwei},
  booktitle = {CVPR},
  year      = {2024}
}

Datasets and Pretrained Models 🛢️

The proposed OSM and GoogleEarth datasets are available as below.

• OSM
• GoogleEarth

The pretrained models are available as below.

• Unbounded Layout Generator
• Background Stuff Generator
• Building Instance Generator

Installation 📥

Assume that you have installed CUDA and PyTorch in your Python (or Anaconda) environment.

The CityDreamer source code is tested in PyTorch 1.13.1 with CUDA 11.7 in Python 3.8. You can use the following command to install PyTorch with CUDA 11.7.

pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 --extra-index-url https://download.pytorch.org/whl/cu117

After that, the Python dependencies can be installed as following.

git clone https://github.com/hzxie/city-dreamer
cd city-dreamer
CITY_DREAMER_HOME=`pwd`
pip install -r requirements.txt

The CUDA extensions can be compiled and installed with the following commands.

cd $CITY_DREAMER_HOME/extensions
for e in `ls -d */`
do
  cd $CITY_DREAMER_HOME/extensions/$e
  pip install .
done

Inference 🚩

Both the iterative demo and command line interface (CLI) by default load the pretrained models for Unbounded Layout Generator, Background Stuff Generator, and Building Instance Generator from output/sampler.pth, output/gancraft-bg.pth, and output/gancraft-fg.pth, respectively. You have the option to specify a different location using runtime arguments.

├── ...
└── city-dreamer
    └── demo
    |   ├── ...
    |   └── run.py
    └── scripts
    |   ├── ...
    |   └── inference.py
    └── output
        ├── gancraft-bg.pth
        ├── gancraft-fg.pth
        └── sampler.pth

Moreover, both scripts feature runtime arguments --patch_height and --patch_width, which divide images into patches of size patch_heightxpatch_width. For a single NVIDIA RTX 3090 GPU with 24GB of VRAM, both patch_height and patch_width are set to 5. You can adjust the values to match your GPU's VRAM size.

Iterative Demo 🕹️

python3 demo/run.py

Then, open http://localhost:3186 in your browser.

Command Line Interface (CLI) 🤖

python3 scripts/inference.py

The generated video is located at output/rendering.mp4.

Training👩🏽‍💻

Dataset Preparation

By default, all scripts load the OSM and GoogleEarth datasets from ./data/osm and ./data/ges, respectively. You have the option to specify a different location using runtime arguments.

├── ...
└── city-dreamer
    └── data
        ├── ges  # GoogleEarth
        └── osm  # OSM

The instance segmentation annotation for the GoogleEarth dataset needs to be generated as following steps (requiring approximately 1TB of disk space).

1. Generate semantic segmentation using SEEM.

git clone -b v1.0 https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once.git
mv Segment-Everything-Everywhere-All-At-Once $CITY_DREAMER_HOME/../SEEM
cd $CITY_DREAMER_HOME/../SEEM
# Remove the PyTorch 2.1.0 dependency. PyTorch 1.13.1 is also OK for SEEM.
sed -i "/torch/d" assets/requirements/requirements.txt
# Install the dependencies for SEEM
pip install -r assets/requirements/requirements.txt
pip install -r assets/requirements/requirements_custom.txt
# Back to the CityDreamer codebase
cd $CITY_DREAMER_HOME
python3 scripts/footage_segmentation.py

2. Generate instance segmetation.

cd $CITY_DREAMER_HOME
python3 scripts/dataset_generator.py

Unbounded Layout Generator Training

Unbounded Layout Generator consists of two networks: VQVAE and Sampler.

Launch Training 🚀

# 0x01. Train VQVAE with 4 GPUs
torchrun --nnodes=1 --nproc_per_node=4 --standalone run.py -n VQGAN -e VQGAN-Exp

# 0x02. Train Sampler with 2 GPUs
torchrun --nnodes=1 --nproc_per_node=2 --standalone run.py -n Sampler -e Sampler-Exp\
         -p output/checkpoints/VQGAN-Exp/ckpt-last.pth

Background Stuff Generator Training

Update config.py ⚙️

Make sure the config matches the following lines.

cfg.NETWORK.GANCRAFT.BUILDING_MODE               = False
cfg.TRAIN.GANCRAFT.REC_LOSS_FACTOR               = 10
cfg.TRAIN.GANCRAFT.PERCEPTUAL_LOSS_FACTOR        = 10
cfg.TRAIN.GANCRAFT.GAN_LOSS_FACTOR               = 0.5

Launch Training 🚀

# 0x03. Train Background Stuff Generator with 8 GPUs
torchrun --nnodes=1 --nproc_per_node=8 --standalone run.py -n GANCraft -e BSG-Exp

Building Instance Generator Training

Update config.py ⚙️

Make sure the config matches the following lines.

cfg.NETWORK.GANCRAFT.BUILDING_MODE               = True
cfg.TRAIN.GANCRAFT.REC_LOSS_FACTOR               = 0
cfg.TRAIN.GANCRAFT.PERCEPTUAL_LOSS_FACTOR        = 0
cfg.TRAIN.GANCRAFT.GAN_LOSS_FACTOR               = 1

Launch Training 🚀

# 0x04. Train Building Instance Generator with 8 GPUs
torchrun --nnodes=1 --nproc_per_node=8 --standalone run.py -n GANCraft -e BIG-Exp

License

This project is licensed under NTU S-Lab License 1.0. Redistribution and use should follow this license.