Layout design is ubiquitous in many applications, e.g. architecture/urban planning, etc, which involves a lengthy iterative design process. Recently, deep learning has been leveraged to automatically generate layouts via image generation, showing a huge potential to free designers from laborious routines. While automatic generation can greatly boost productivity, designer input is undoubtedly crucial. An ideal AI-aided design tool should automate repetitive routines, and meanwhile accept human guidance and provide smart/proactive suggestions. However, the capability of involving humans into the loop has been largely ignored in existing methods which are mostly end-to-end approaches. To this end, we propose a new human-in-the-loop generative model, iPLAN, which is capable of automatically generating layouts, but also interacting with designers throughout the whole procedure, enabling humans and AI to co-evolve a sketchy idea gradually into the final design. iPLAN is evaluated on diverse datasets and compared with existing methods. The results show that iPLAN has high fidelity in producing similar layouts to those from human designers, great flexibility in accepting designer inputs and providing design suggestions accordingly, and strong generalizability when facing unseen design tasks and limited training data.
Below is the key environment under which the code was developed, not necessarily the minimal requirements:
- Python 3.6.13
- pytorch 1.7.0
- cuda 10.1
And other libraries such as numpy.
Download RPLAN from here and each data can be processed into the following format:
data_item
- name: file name in RPLAN dataset
- boundary: (x, y, dir, isNew), external boundary and first two point indicate the front door
- gt_rTypes: room categories
- gt_rBoxes: (y0,x0,y1,x1), left-top(in) and right-bottom(out) corner of a room bounding box, extracted directly from the RPLAN dataset
Train the models for predicting room types, locating room and partitioning room in sequence.
Run the training script, the experiment logs and outputs are saved in experiment.
For example:
cd room_type
python train_roomtype.py
The trained model can be download here for test.
Feixiang He, Yanlong Huang, He Wang
Feixiang He, scfh@leeds.ac.uk
He Wang, h.e.wang@leeds.ac.uk, Personal website
Project Webpage: http://drhewang.com/pages/iplan.html
If you have any questions, please contact me: Feixiang He(scfh@leeds.ac.uk)
Toolbox to render floorplan: https://github.com/zzilch/RPLAN-Toolbox
Original RPLAN dataset: http://staff.ustc.edu.cn/~fuxm/projects/DeepLayout/index.html
Please cite our paper if you find it useful:
@article{he2022iplan,
title={iPLAN: Interactive and Procedural Layout Planning},
author={He, Feixiang and Huang, Yanlong and Wang, He},
journal={The Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}
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