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
Source code for TACL paper "KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation".

KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation Source code for TACL 2021 paper KEPLER: A Unified Model for Kn

THU-KEG 138 Dec 22, 2022
tree-math: mathematical operations for JAX pytrees

tree-math: mathematical operations for JAX pytrees tree-math makes it easy to implement numerical algorithms that work on JAX pytrees, such as iterati

Google 137 Dec 28, 2022
A `Neural = Symbolic` framework for sound and complete weighted real-value logic

Logical Neural Networks LNNs are a novel Neuro = symbolic framework designed to seamlessly provide key properties of both neural nets (learning) and s

International Business Machines 138 Dec 19, 2022
A python package for generating, analyzing and visualizing building shadows

pybdshadow Introduction pybdshadow is a python package for generating, analyzing and visualizing building shadows from large scale building geographic

Qing Yu 13 Nov 30, 2022
Deep Learning Theory

Deep Learning Theory 整理了一些深度学习的理论相关内容,持续更新。 Overview Recent advances in deep learning theory 总结了目前深度学习理论研究的六个方向的一些结果,概述型,没做深入探讨(2021)。 1.1 complexity

fq 103 Jan 04, 2023
Deep Sea Treasure Environment for Multi-Objective Optimization Research

DeepSeaTreasure Environment Installation In order to get started with this environment, you can install it using the following command: python3 -m pip

imec IDLab 6 Nov 14, 2022
Efficiently computes derivatives of numpy code.

Note: Autograd is still being maintained but is no longer actively developed. The main developers (Dougal Maclaurin, David Duvenaud, Matt Johnson, and

Formerly: Harvard Intelligent Probabilistic Systems Group -- Now at Princeton 6.1k Jan 08, 2023
Deep Compression for Dense Point Cloud Maps.

DEPOCO This repository implements the algorithms described in our paper Deep Compression for Dense Point Cloud Maps. How to get started (using Docker)

Photogrammetry & Robotics Bonn 67 Dec 06, 2022
DeepSpamReview: Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures. Summer Internship project at CoreView Systems.

Detection of Fake Reviews on Online Review Platforms using Deep Learning Architectures Dataset: https://s3.amazonaws.com/fast-ai-nlp/yelp_review_polar

Ashish Salunkhe 37 Dec 17, 2022
Public Code for NIPS submission SimiGrad: Fine-Grained Adaptive Batching for Large ScaleTraining using Gradient Similarity Measurement

Public code for NIPS submission "SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement" This repo co

Heyang Qin 0 Oct 13, 2021
Classification of EEG data using Deep Learning

Graduation-Project Classification of EEG data using Deep Learning Epilepsy is the most common neurological disease in the world. Epilepsy occurs as a

Osman Alpaydın 5 Jun 24, 2022
code for our ECCV 2020 paper "A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation"

Code for our ECCV (2020) paper A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation. Prerequisites: python == 3.6.8 pytorch ==1.1.0

32 Nov 27, 2022
Python package for visualizing the loss landscape of parameterized quantum algorithms.

orqviz A Python package for easily visualizing the loss landscape of Variational Quantum Algorithms by Zapata Computing Inc. orqviz provides a collect

Zapata Computing, Inc. 75 Dec 30, 2022
Sematic-Segmantation - Semantic Segmentation on MIT ADE20K dataset in PyTorch

Semantic Segmentation on MIT ADE20K dataset in PyTorch This is a PyTorch impleme

Berat Eren Terzioğlu 4 Mar 22, 2022
Cosine Annealing With Warmup

CosineAnnealingWithWarmup Formulation The learning rate is annealed using a cosine schedule over the course of learning of n_total total steps with an

zhuyun 4 Apr 18, 2022
An original implementation of "Noisy Channel Language Model Prompting for Few-Shot Text Classification"

Channel LM Prompting (and beyond) This includes an original implementation of Sewon Min, Mike Lewis, Hannaneh Hajishirzi, Luke Zettlemoyer. "Noisy Cha

Sewon Min 92 Jan 07, 2023
Vit-ImageClassification - Pytorch ViT for Image classification on the CIFAR10 dataset

Vit-ImageClassification Introduction This project uses ViT to perform image clas

Kaicheng Yang 4 Jun 01, 2022
Official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR)

This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.

12 Jan 13, 2022
Image Segmentation and Object Detection in Pytorch

Image Segmentation and Object Detection in Pytorch Pytorch-Segmentation-Detection is a library for image segmentation and object detection with report

Daniil Pakhomov 732 Dec 10, 2022
Source codes for "Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs"

Structure-Aware-BART This repo contains codes for the following paper: Jiaao Chen, Diyi Yang:Structure-Aware Abstractive Conversation Summarization vi

GT-SALT 56 Dec 08, 2022