MaskPLAN: Masked Generative Layout Planning from Partial Input

Hang Zhang, Anton Savov, Benjamin Dillenburger

Digital Building Technologies, ETH Zurich

MaskPLAN allows users to influence layout generation with just the features they prioritize, using partial inputs in a Graph-structured Dynamic Masked Autoencoder (GDMAE), predicting layouts from incomplete design ideas.

Video:

1. install the requirements:

Clone the repo and install conda env:

cd MaskPLAN
conda env create -f MaskPLAN.yaml
conda activate MaskPLAN

System requirements:

• a. GPU with at least 24GB Memory.
• b. System RAM at least 32GB.
• c. Hard-drive free storage at least 20GB.

2. prepare the dataset:

The original RPLAN dataset is from RPLAN project page. Our data preprocess inherited the implementation from RPLAN-Toolbox and Graph2Plan-DataPreparation. We've offered the pre-processed data in folder Processed_data. If you would like to walk through our data processing, follow the steps:

• a. download the dataset from this link, put in [your_downloaded_path]. run through the RPLAN_DataProcess.ipynb in folder Processed_data, where the dataset path should follow [your_downloaded_path].

• b. pretraining the vqvae by run through the VQVAE_Prior_Training_5564_B.ipynb and VQVAE_Prior_Training_5564_R.ipynb in folder VQVAE_Pretrain. We've also offered our checkpoints, see folder VQ_Pretrained.

• c. run dynamic masking data processing in folder Processed_data. We offer the process of vector-only attributes representation (as ablation), and the img-vec hybrid attributes representation (as in our paper).

# data in vector-only format
python Processed_data/FP_DataProcess_vec.py

# data in vec-img hybrid format
python Processed_data/FP_DataProcess_cross.py

We've offered our checkpoints in this link with four ablations (two model sizes with vector-only or vec-img hybrid data). If you would like to train the model, run the following code:

# training small mdoel in vector-only format
python Train/MaskPLAN_Train_Allvec.py

# training large mdoel in vector-only format
python Train/MaskPLAN_Train_AllvecDeep.py

# training small mdoel in vec-img hybrid format
python Train/MaskPLAN_Train_Allcross.py

# training large mdoel in vec-img hybrid format
python Train/MaskPLAN_Train_AllcrossDeep.py

1. fast test iteration:

We offer fast test with random sampled iterations.

# Inference small mdoel in vector-only format
python Inference/MaskPLAN_Inference_iterate_vec_Single.py

# Inference large mdoel in vector-only format
python Inference/MaskPLAN_Inference_iterate_vec_Deep.py

# Inference small mdoel in vec-img hybrid format
python Inference/MaskPLAN_Inference_iterate_cross_Single.py

# Inference large mdoel in vec-img hybrid format
python Inference/MaskPLAN_Inference_iterate_cross_Deep.py

2. UI interface:

real-time UI interface.

• requirement: install Rhino 8.
• code: TODO

@inproceedings{zhang2024maskplan,
  title={MaskPLAN: Masked Generative Layout Planning from Partial Input},
  author={Zhang, Hang and Savov, Anton and Dillenburger, Benjamin},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={8964--8973},
  year={2024}
}