December 11, 2020

273 words 2 mins read

iPERDance/iPERCore

iPERDance/iPERCore

Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis

repo name iPERDance/iPERCore
repo link https://github.com/iPERDance/iPERCore
homepage https://www.impersonator.org/work/impersonator-plus-plus.html
language Python
size (curr.) 11938 kB
stars (curr.) 541
created 2020-11-17
license

Impersonator++

Update News

  • 12/20/2020, A precompiled version on Windows has been released! [Usage]
  • 12/10/2020, iPERCore-0.1.1, supports Windows.
  • 12/06/2020, iPERCore-0.1, all the base codes. The motion imitation scripts.

See the details of developing logs.

Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis, including human motion imitation, appearance transfer, and novel view synthesis. Currently the paper is under review of IEEE TPAMI. It is an extension of our previous ICCV project impersonator, and it has a more powerful ability in generalization and produces higher-resolution results (512 x 512, 1024 x 1024) than the previous ICCV version.

🧾 Colab Notebook Released (Windows) 📑 Paper 📱 Website 📂 Dataset 💡 Bilibili ✒ Forum
Open In Colab [Usage] paper website Dataset bilibili Forum

Installation

See more details, including system dependencies, python requirements and setups in install.md. Please follows the instructions in install.md to install this firstly.

Run demos

1. Run on Google Colab

Open In Colab

2. Run with Console (scripts) mode

See scripts_runner for more details.

3. Run with GUI mode

Coming soon!

Citation

@misc{liu2020liquid,
      title={Liquid Warping GAN with Attention: A Unified Framework for Human Image Synthesis}, 
      author={Wen Liu and Zhixin Piao, Zhi Tu, Wenhan Luo, Lin Ma and Shenghua Gao},
      year={2020},
      eprint={2011.09055},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@InProceedings{lwb2019,
    title={Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesis},
    author={Wen Liu and Zhixin Piao, Min Jie, Wenhan Luo, Lin Ma and Shenghua Gao},
    booktitle={The IEEE International Conference on Computer Vision (ICCV)},
    year={2019}
}
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