Paper: De-rendering Stylized Texts

Overview

Paper: De-rendering Stylized Texts

Wataru Shimoda1, Daichi Haraguchi2, Seiichi Uchida2, Kota Yamaguchi1
1CyberAgent.Inc, 2 Kyushu University
Accepted to ICCV2021. [Publication] [Arxiv] [project-page]

Introduction

This repository contains the codes for "De-rendering stylized texts".

Concept

We propose to parse rendering parameters of stylized texts utilizing a neural net.

Demo

The proposed model parses rendering parameters based on famous 2d graphic engine[Skia.org|python implementation], which has compatibility with CSS in the Web. We can export the estimated rendering parameters and edit texts by an off-the-shelf rendering engine.

Installation

Requirements

  • Python >= 3.7
  • Pytorch >= 1.8.1
  • torchvision >= 0.9.1
pip install -r requiements.txt

Font data

  • The proposed model is trained with google fonts.
  • Download google fonts and locate in data/fonts/ as gfonts.
cd data/fonts
git clone https://github.com/google/fonts.git gfonts

Pre-rendered alpha maps

  • The proposed model parses rendering parameters and refines them through the differentiable rendering model, which uses pre-rendered alpha maps.
  • Generate pre-rendered alpha maps.
python -m util_lib.gen_pams

Pre-rendered alpha maps would be generated in data/fonts/prerendered_alpha.

Usage

Test

  • Download the pre-trained weight from this link (weight).
  • Locate the weight file in weights/font100_unified.pth.

Example usage.

python test.py --imgfile=example/sample.jpg

Note

  • imgfile option: path of an input image
  • results would be generated in res/

Data generation

in progress

Train

in progress

Todo

  • Testing codes
  • Codes for the text image generator
  • Training codes
  • Add notebooks for the guide

Reference

@InProceedings{Shimoda_2021_ICCV,
    author    = {Shimoda, Wataru and Haraguchi, Daichi and Uchida, Seiichi and Yamaguchi, Kota},
    title     = {De-Rendering Stylized Texts},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {1076-1085}
}

Contact

This repository is maintained by Wataru shimoda(wataru_shimoda[at]cyberagent.co.jp).

Owner
CyberAgent AI Lab
CyberAgent AI Lab
Ready-to-use code and tutorial notebooks to boost your way into few-shot image classification.

Easy Few-Shot Learning Ready-to-use code and tutorial notebooks to boost your way into few-shot image classification. This repository is made for you

Sicara 399 Jan 08, 2023
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

Introduction This is a Python package available on PyPI for NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pyto

Artit 'Art' Wangperawong 5 Sep 29, 2021
Bayesian regularization for functional graphical models.

BayesFGM Paper: Jiajing Niu, Andrew Brown. Bayesian regularization for functional graphical models. Requirements R version 3.6.3 and up Python 3.6 and

0 Oct 07, 2021
A Pythonic library for Nvidia Codec.

A Pythonic library for Nvidia Codec. The project is still in active development; expect breaking changes. Why another Python library for Nvidia Codec?

Zesen Qian 12 Dec 27, 2022
Table-Extractor 表格抽取

(t)able-(ex)tractor 本项目旨在实现pdf表格抽取。 Models 版面分析模块(Yolo) 表格结构抽取(ResNet + Transformer) 文字识别模块(CRNN + CTC Loss) Acknowledgements TableMaster attention-i

2 Jan 15, 2022
Predicting Event Memorability from Contextual Visual Semantics

Predicting Event Memorability from Contextual Visual Semantics

0 Oct 06, 2021
Language model Prompt And Query Archive

LPAQA: Language model Prompt And Query Archive This repository contains data and code for the paper How Can We Know What Language Models Know? Install

127 Dec 20, 2022
Progressive Coordinate Transforms for Monocular 3D Object Detection

Progressive Coordinate Transforms for Monocular 3D Object Detection This repository is the official implementation of PCT. Introduction In this paper,

58 Nov 06, 2022
The codebase for Data-driven general-purpose voice activity detection.

Data driven GPVAD Repository for the work in TASLP 2021 Voice activity detection in the wild: A data-driven approach using teacher-student training. S

Heinrich Dinkel 75 Nov 27, 2022
基于Paddlepaddle复现yolov5,支持PaddleDetection接口

PaddleDetection yolov5 https://github.com/Sharpiless/PaddleDetection-Yolov5 简介 PaddleDetection飞桨目标检测开发套件,旨在帮助开发者更快更好地完成检测模型的组建、训练、优化及部署等全开发流程。 PaddleD

36 Jan 07, 2023
Self-Supervised depth kalilia

Self-Supervised depth kalilia

24 Oct 15, 2022
Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research

Welcome to AirSim AirSim is a simulator for drones, cars and more, built on Unreal Engine (we now also have an experimental Unity release). It is open

Microsoft 13.8k Jan 03, 2023
Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch

Enformer - Pytorch (wip) Implementation of Enformer, Deepmind's attention network for predicting gene expression, in Pytorch. The original tensorflow

Phil Wang 235 Dec 27, 2022
Unified MultiWOZ evaluation scripts for the context-to-response task.

MultiWOZ Context-to-Response Evaluation Standardized and easy to use Inform, Success, BLEU ~ See the paper ~ Easy-to-use scripts for standardized eval

Tomáš Nekvinda 38 Dec 13, 2022
Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations, CVPR 2019 (Oral)

Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations The code of: Weakly Supervised Learning of Instance Segmentation with I

Jiwoon Ahn 472 Dec 29, 2022
In this work, we will implement some basic but important algorithm of machine learning step by step.

WoRkS continued English 中文 Français Probability Density Estimation-Non-Parametric Methods(概率密度估计-非参数方法) 1. Kernel / k-Nearest Neighborhood Density Est

liziyu0104 1 Dec 30, 2021
Object classification with basic computer vision techniques

naive-image-classification Object classification with basic computer vision techniques. Final assignment for the computer vision course I took at univ

2 Jul 01, 2022
Object tracking and object detection is applied to track golf puts in real time and display stats/games.

Putting_Game Object tracking and object detection is applied to track golf puts in real time and display stats/games. Works best with the Perfect Prac

Max 1 Dec 29, 2021
Pseudo-Visual Speech Denoising

Pseudo-Visual Speech Denoising This code is for our paper titled: Visual Speech Enhancement Without A Real Visual Stream published at WACV 2021. Autho

Sindhu 94 Oct 22, 2022
Ansible Automation Example: JSNAPY PRE/POST Upgrade Validation

Ansible Automation Example: JSNAPY PRE/POST Upgrade Validation Overview This example will show how to validate the status of our firewall before and a

Calvin Remsburg 1 Jan 07, 2022