Y1ran / NewSeaCity-AI-Media-Transfer

基于DCT-NET风格迁移算法的视频自动化风格修改demo

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NewSeaCity-AI-Media-Transfer

基于DCT-NET风格迁移算法的视频自动化风格修改demo,源项目:https://github.com/menyifang/DCT-Net

DCT-Net: Domain-Calibrated Translation for Portrait Stylization, Yifang Men1, Yuan Yao1, Miaomiao Cui1, Zhouhui Lian2, Xuansong Xie1,
1DAMO Academy, Alibaba Group, Beijing, China
2Wangxuan Institute of Computer Technology, Peking University, China
In: SIGGRAPH 2022 (TOG) arXiv preprint

google colab logo Hugging Face Spaces

Demo

demo_vid

Requirements

  • python >= 3.7
  • tensorflow >=1.14
  • CuDNN == 11.3.1
  • CUDA == 8.1.0
  • easydict
  • numpy
  • both CPU/GPU are supported

Quick Start

  • 下载并安装ModelScope library
conda create -n dctnet python=3.8
conda activate dctnet
conda install tensorflow==2.10
conda install "modelscope[cv]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
  • 模型加载和推理demo
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('image-portrait-stylization', 'damo/cv_unet_person-image-cartoon_compound-models')
  • 运行视频转绘画风格demo
python demo.py

Multi-style

Multi-style models and usages are provided here.

demo_img

git clone https://github.com/menyifang/DCT-Net.git
cd DCT-Net

Multi-style models download

  • upgrade modelscope>=0.4.7
conda activate dctnet
pip install --upgrade "modelscope[cv]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html
  • Download the pretrained models with specific styles [option: anime, 3d, handdrawn, sketch, artstyle]
python multi-style/download.py --style 3d

Inference

  • Quick infer with python SDK, style choice [option: anime, 3d, handdrawn, sketch, artstyle]
python multi-style/run_sdk.py --style 3d
  • Infer from source code & downloaded models
python multi-style/run.py --style 3d

Reference

@inproceedings{men2022dct,
  title={DCT-Net: Domain-Calibrated Translation for Portrait Stylization},
  author={Men, Yifang and Yao, Yuan and Cui, Miaomiao and Lian, Zhouhui and Xie, Xuansong},
  journal={ACM Transactions on Graphics (TOG)},
  volume={41},
  number={4},
  pages={1--9},
  year={2022},
  publisher={ACM New York, NY, USA}
}

About

基于DCT-NET风格迁移算法的视频自动化风格修改demo

License:Apache License 2.0


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Language:Python 100.0%