[MM’21] Constrained Graphic Layout Generation via Latent Optimization

This repository provides the official code for the paper “Constrained Graphic Layout Generation via Latent Optimization”, especially the code for:

  • LayoutGAN++: generative adversarial networks for layout generation
  • CLG-LO: a framework for generating layouts that satisfy constraints
  • Layout evaluation: measuring the quantitative metrics of Layout FID, Maximum IoU, Alignment, and Overlap for generated layouts


Clone this repositorygit clone https://github.com/ktrk115/const_layout.git cd const_layout

Create a new conda environment (Python 3.8)conda create -n const_layout python=3.8 conda activate const_layout

Install PyTorch 1.8.* and the corresponding versoin of PyTorch Geometric

Install the other dependent librariespip install -r requirements.txt

Prepare data (see this instruction)

Download pre-trained models./download_model.sh

Development environment

  • Ubuntu 18.04, CUDA 11.1



Training animation

Generate layouts with LayoutGAN++

python generate.py pretrained/layoutganpp_rico.pth.tar --out_path output/generated_layouts.pkl --num_save 5

Train LayoutGAN++ model

python train.py --dataset rico --batch_size 64 --iteration 200000 --latent_size 4 --lr 1e-05 --G_d_model 256 --G_nhead 4 --G_num_layers 8 --D_d_model 256 --D_nhead 4 --D_num_layers 8


w/ beautification constraints w/ relational constraints

Generate layouts with beautification constraints

python generate_const.py pretrained/layoutganpp_publaynet.pth.tar --const_type beautify --out_path output/beautify/generated_layouts.pkl --num_save 5

Generate layouts with relational constraints

python generate_const.py pretrained/layoutganpp_publaynet.pth.tar --const_type relation --out_path output/relation/generated_layouts.pkl --num_save 5

Layout evaluation

Evaluate generated layouts

python eval.py rico output/generated_layouts.pkl

A pickle file should be a list of layouts, where each layout is a tuple of bounding boxes and labels. The bounding box is represented by [x, y, width, height] in normalized coordinates, and the label is represented by an index. An example is shown below.

In [x]: layouts
[(array([[0.47403812, 0.11276676, 0.6250037 , 0.02210438],
         [0.49971417, 0.8550553 , 0.81388366, 0.03492427],
         [0.49919674, 0.47857162, 0.81024694, 0.7070079 ]], dtype=float32),
  array([0, 0, 3]),


If this repository helps your research, please consider citing our paper.

    title = {Constrained Graphic Layout Generation via Latent Optimization},
    author = {Kotaro Kikuchi and Edgar Simo-Serra and Mayu Otani and Kota Yamaguchi},
    booktitle = {Proceedings of the ACM International Conference on Multimedia},
    series = {MM '21},
    volume = {},
    year = {2021},
    pages = {},
    doi = {10.1145/3474085.3475497}



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