Skip to content
A painting AI that can reproduce paintings stroke by stroke using deep reinforcement learning.
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
baseline Fix modelfree and training log May 16, 2019
baseline_modelfree Add small noise to model-free training May 16, 2019
demo Update README.md Apr 25, 2019
image Update README.md Apr 25, 2019
.gitignore Make videos Apr 25, 2019
LICENSE Create LICENSE Apr 25, 2019
LearningToPaint.ipynb Modify demo Apr 25, 2019
README.md Fix modelfree and training log May 16, 2019

README.md

Learning to Paint

Arxiv | YouTube | Reddit

Abstract

We show how to teach machines to paint like human painters, who can use a few strokes to create fantastic paintings. By combining the neural renderer and model-based Deep Reinforcement Learning (DRL), our agent can decompose texture-rich images into strokes and make long-term plans. For each stroke, the agent directly determines the position and color of the stroke. Excellent visual effect can be achieved using hundreds of strokes. The training process does not require experience of human painting or stroke tracking data.

You can easily use colaboratory to have a try.

DemoDemoDemo DemoDemoDemo

Architecture

Installation

Use anaconda to manage environment

$ conda create -n py36 python=3.6
$ source activate py36
$ git clone https://github.com/hzwer/LearningToPaint.git
$ cd LearningToPaint

Dependencies

pip3 install torch==0.4.1
pip3 install tensorboardX
pip3 install opencv-python

Testing

Make sure there are renderer.pkl and actor.pkl before testing.

You can download a trained neural renderer and a CelebA actor for test: renderer.pkl and actor.pkl

$ wget "https://drive.google.com/uc?export=download&id=1-7dVdjCIZIxh8hHJnGTK-RA1-jL1tor4" -O renderer.pkl
$ wget "https://drive.google.com/uc?export=download&id=1a3vpKgjCVXHON4P7wodqhCgCMPgg1KeR" -O actor.pkl
$ python3 baseline/test.py --max_step=100 --actor=actor.pkl --renderer=renderer.pkl --img=image/test.png --divide=4
$ ffmpeg -r 10 -f image2 -i output/generated%d.png -s 512x512 -c:v libx264 -pix_fmt yuv420p video.mp4 -q:v 0 -q:a 0
(make a painting process video)

We also provide with some other neural renderers and agents, you can use them instead of renderer.pkl to train the agent:

triangle.pkl --- actor_triangle.pkl;

round.pkl --- actor_round.pkl;

bezierwotrans.pkl --- actor_notrans.pkl

Training

Datasets

Download the CelebA dataset and put the aligned images in data/img_align_celeba/******.jpg

Neural Renderer

To create a differentiable painting environment, we need train the neural renderer firstly.

$ python3 baseline/train_renderer.py
$ tensorboard --logdir train_log --port=6006
(The training process will be shown at http://127.0.0.1:6006)

Paint Agent

After the neural renderer looks good enough, we can begin training the agent.

$ cd baseline
$ python3 train.py --max_step=40 --debug --batch_size=96
(A step contains 5 strokes in default.)
$ tensorboard --logdir train_log --port=6006

Results

Painting process in different datasets

FAQ

Why does your demo look better than the result in your paper?

In our demo, after painting the outline of each image, we divide it into small patches to paint parallelly to get a high resolution.

Your main difference from primitive

Our research is to explore how to make machines learn to use painting tools. Our implementation is a combination of reinforcement learning and computer vision. Please read our paper for more details.

Resources

Contributors

Also many thanks to ctmakro for inspiring this work.

If you find this repository useful for your research, please cite the following paper:

@article{huang2019learning,
  title={Learning to Paint with Model-based Deep Reinforcement Learning},
  author={Huang, Zhewei and Heng, Wen and Zhou, Shuchang},
  journal={arXiv preprint arXiv:1903.04411},
  year={2019}
}
You can’t perform that action at this time.