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Paint Transformer: Feed Forward Neural Painting with Stroke Prediction

[Paper] [Paddle Implementation]

Update

We have optimized the serial inference procedure to achieve better rendering quality and faster speed.

Overview

This repository contains the official PaddlePaddle implementation of paper:

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction,

Songhua Liu*, Tianwei Lin*, Dongliang He, Fu Li, Ruifeng Deng, Xin Li, Errui Ding, Hao Wang (* indicates equal contribution)

ICCV 2021 (Oral)

Prerequisites

  • Linux or macOS

  • Python 3.6+

  • PaddlePaddle 2.0+ and other dependencies (numpy, cv2, and other common python libs)

    python -m pip install paddlepaddle-gpu

Getting Started

  • Clone this repository:

    git clone https://github.com/wzmsltw/PaintTransformer
cd PaintTransformer

  • Download pretrained model from Google Drive and move it to inference directory:

    mv [Download Directory]/paint_best.pdparams inference/
cd inference

  • Inference:

    python inference.py
    • Input image path, output path, and etc can be set in the main function.
    • Notably, there is a flag serial as one parameter of the main function:
      • If serial is True, strokes would be rendered serially. The consumption of video memory will be low but it requires more time. Serial inference can achieve better rendering quality.
      • If serial is False, strokes would be rendered in parallel. The consumption of video memory will be high but it would be faster.
      • If animated results are required, serial must be True.

  • Train:

    • You can send email to us for the training codes.

More Results

Input Animated Output

App

Citation

  • If you find ideas or codes useful for your research, please cite:

    @inproceedings{liu2021paint,
  title={Paint Transformer: Feed Forward Neural Painting with Stroke Prediction},
  author={Liu, Songhua and Lin, Tianwei and He, Dongliang and Li, Fu and Deng, Ruifeng and Li, Xin and Ding, Errui and Wang, Hao},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  year={2021}
}

Contact

For any question, please file an issue or contact

Songhua Liu: songhua.liu@smail.nju.edu.cn
Tianwei Lin: lintianwei01@baidu.com