A sketch extractor for anime/illustration.

Overview

Anime2Sketch

Anime2Sketch: A sketch extractor for illustration, anime art, manga

By Xiaoyu Xiang

teaser demo

Updates

  • 2021.5.2: Upload more example results of anime video.
  • 2021.4.30: Upload the test scripts. Now our repo is ready to run!
  • 2021.4.11: Upload the pretrained weights, and more test results.
  • 2021.4.8: Create the repo.

Introduction

The repository contains the testing codes and pretrained weights for Anime2Sketch.

Anime2Sketch is a sketch extractor that works well on illustration, anime art, and manga. It is an application based on the paper "Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis".

Prerequisites

Get Started

Installation

Install the required packages: pip install -r requirements.txt

Download Pretrained Weights

Please download the weights from GoogleDrive, and put it into the weights/ folder.

Test

python3 test.py --dataroot /your_input/dir --load_size 512 --output_dir /your_output/dir

The above command includes three arguments:

  • dataroot: your test file or directory
  • load_size: due to the memory limit, we need to resize the input image before processing. By default, we resize it to 512x512.
  • output_dir: path of the output directory

Run our example:

python3 test.py --dataroot test_samples/madoka.jpg --load_size 512 --output_dir results/

Train

This project is a sub-branch of AODA. Please check it for the training instructions.

More Results

Our model works well on illustration arts: madoka demo demo1 Turn handrawn photos to clean linearts: demo2 Simplify freehand sketches: demo3 And more anime results: demo4 demo5

Contact

Xiaoyu Xiang.

You can also leave your questions as issues in the repository. I will be glad to answer them!

License

This project is released under the MIT License.

Citations

@misc{Anime2Sketch,
  author = {Xiaoyu Xiang, Ding Liu, Xiao Yang, Yiheng Zhu, Xiaohui Shen},
  title = {Anime2Sketch: A Sketch Extractor for Anime Arts with Deep Networks},
  year = {2021},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/Mukosame/Anime2Sketch}}
}

@misc{xiang2021adversarial,
      title={Adversarial Open Domain Adaption for Sketch-to-Photo Synthesis}, 
      author={Xiang, Xiaoyu and Liu, Ding and Yang, Xiao and Zhu, Yiheng and Shen, Xiaohui and Allebach, Jan P},
      year={2021},
      eprint={2104.05703},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
Owner
Xiaoyu Xiang
Ph.D. candidate at Purdue University.
Xiaoyu Xiang
Company clustering with K-means/GMM and visualization with PCA, t-SNE, using SSAN relation extraction

RE results graph visualization and company clustering Installation pip install -r requirements.txt python -m nltk.downloader stopwords python3.7 main.

Jieun Han 1 Oct 06, 2022
A PyTorch Reimplementation of TecoGAN: Temporally Coherent GAN for Video Super-Resolution

TecoGAN-PyTorch Introduction This is a PyTorch reimplementation of TecoGAN: Temporally Coherent GAN for Video Super-Resolution (VSR). Please refer to

165 Dec 17, 2022
Code implementing "Improving Deep Learning Interpretability by Saliency Guided Training"

Saliency Guided Training Code implementing "Improving Deep Learning Interpretability by Saliency Guided Training" by Aya Abdelsalam Ismail, Hector Cor

8 Sep 22, 2022
PyTorch implementation of MSBG hearing loss model and MBSTOI intelligibility metric

PyTorch implementation of MSBG hearing loss model and MBSTOI intelligibility metric This repository contains the implementation of MSBG hearing loss m

BUT <a href=[email protected]"> 9 Nov 08, 2022
Node for thenewboston digital currency network.

Project setup For project setup see INSTALL.rst Community Join the community to stay updated on the most recent developments, project roadmaps, and ra

thenewboston 27 Jul 08, 2022
Pseudo-mask Matters in Weakly-supervised Semantic Segmentation

Pseudo-mask Matters in Weakly-supervised Semantic Segmentation By Yi Li, Zhanghui Kuang, Liyang Liu, Yimin Chen, Wayne Zhang SenseTime, Tsinghua Unive

33 Oct 14, 2022
Code for the ICASSP-2021 paper: Continuous Speech Separation with Conformer.

Continuous Speech Separation with Conformer Introduction We examine the use of the Conformer architecture for continuous speech separation. Conformer

Sanyuan Chen (陈三元) 81 Nov 28, 2022
A Python package to process & model ChEMBL data.

insilico: A Python package to process & model ChEMBL data. ChEMBL is a manually curated chemical database of bioactive molecules with drug-like proper

Steven Newton 0 Dec 09, 2021
Semiconductor Machine learning project

Wafer Fault Detection Problem Statement: Wafer (In electronics), also called a slice or substrate, is a thin slice of semiconductor, such as a crystal

kunal suryawanshi 1 Jan 15, 2022
I tried to apply the CAM algorithm to YOLOv4 and it worked.

YOLOV4:You Only Look Once目标检测模型在pytorch当中的实现 2021年2月7日更新: 加入letterbox_image的选项,关闭letterbox_image后网络的map得到大幅度提升。 目录 性能情况 Performance 实现的内容 Achievement

55 Dec 05, 2022
An off-line judger supporting distributed problem repositories

Thaw 中文 | English Thaw is an off-line judger supporting distributed problem repositories. Everyone can use Thaw release problems with license on GitHu

countercurrent_time 2 Jan 09, 2022
CVNets: A library for training computer vision networks

CVNets: A library for training computer vision networks This repository contains the source code for training computer vision models. Specifically, it

Apple 1.1k Jan 03, 2023
Pytorch Implementation of Residual Vision Transformers(ResViT)

ResViT Official Pytorch Implementation of Residual Vision Transformers(ResViT) which is described in the following paper: Onat Dalmaz and Mahmut Yurt

ICON Lab 41 Dec 08, 2022
A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

A resource for learning about ML, DL, PyTorch and TensorFlow. Feedback always appreciated :)

Aladdin Persson 4.7k Jan 08, 2023
Train SN-GAN with AdaBelief

SNGAN-AdaBelief Train a state-of-the-art spectral normalization GAN with AdaBelief https://github.com/juntang-zhuang/Adabelief-Optimizer Acknowledgeme

Juntang Zhuang 10 Jun 11, 2022
MoCap-Solver: A Neural Solver for Optical Motion Capture Data

MoCap-Solver is a data-driven-based robust marker denoising method, which takes raw mocap markers as input and outputs corresponding clean markers and skeleton motions.

55 Dec 28, 2022
OMLT: Optimization and Machine Learning Toolkit

OMLT is a Python package for representing machine learning models (neural networks and gradient-boosted trees) within the Pyomo optimization environment.

C⚙G - Imperial College London 179 Jan 02, 2023
Submanifold sparse convolutional networks

Submanifold Sparse Convolutional Networks This is the PyTorch library for training Submanifold Sparse Convolutional Networks. Spatial sparsity This li

Facebook Research 1.8k Jan 06, 2023
Code for LIGA-Stereo Detector, ICCV'21

LIGA-Stereo Introduction This is the official implementation of the paper LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based

Xiaoyang Guo 75 Dec 09, 2022
Learning infinite-resolution image processing with GAN and RL from unpaired image datasets, using a differentiable photo editing model.

Exposure: A White-Box Photo Post-Processing Framework ACM Transactions on Graphics (presented at SIGGRAPH 2018) Yuanming Hu1,2, Hao He1,2, Chenxi Xu1,

Yuanming Hu 719 Dec 29, 2022