MF Net

MF-NET

MF-NET : 새로운 몇 샷 양식화 된 다국어 글꼴 생성 방법

ACM Digital Library의 논문을 확인하십시오 : https://doi.org/10.1145/3503161.3548414

• 저자 : Yufan Zhang, Junkai Man & Peng Sun*
• 멀티미디어에 관한 30 번째 ACM 국제 회의에서 발표 된 논문 (ACM Multimedia 22)

보충 자원

주관적 평가 조사 - 결과

프로젝트 갤러리

네트워크 구조

결과

프레젠테이션

비디오 프레젠테이션

비디오 프레젠테이션 - 슬라이드

포스터 프레젠테이션 - 포스터

우리를 인용하십시오

 @inproceedings{10.1145/3503161.3548414,
   author = {Zhang, Yufan and Man, Junkai and Sun, Peng},
   title = {MF-Net: A Novel Few-Shot Stylized Multilingual Font Generation Method},
   year = {2022},
   isbn = {9781450392037},
   publisher = {Association for Computing Machinery},
   address = {New York, NY, USA},
   url = {https://doi.org/10.1145/3503161.3548414},
   doi = {10.1145/3503161.3548414},
   abstract = {Creating a complete stylized font library that helps the audience to perceive information from the text often requires years of study and proficiency in the use of many professional tools. Accordingly, automatic stylized font generation in a deep learning-based fashion is a desirable but challenging task that has attracted a lot of attention in recent years. This paper revisits the state-of-the-art methods for stylized font generation and presents a taxonomy of the deep learning-based stylized font generation. Despite the notable performance of the existing models, stylized multilingual font generation, the task of applying specific font style to diverse characters in multiple languages has never been reported to be addressed. An efficient and economical method for stylized multilingual font generation is essential in numerous application scenarios that require communication with international audiences. We propose a solution for few-shot multilingual stylized font generation by a fast feed-forward network, Multilingual Font Generation Network (MF-Net), which can transfer previously unseen font styles from a few samples to characters from previously unseen languages. Following the Generative Adversarial Network (GAN) framework, MF-Net adopts two separate encoders in the generator to decouple a font image's content and style information. We adopt an attention module in the style encoder to extract both shallow and deep style features. Moreover, we also design a novel language complexity-aware skip connection to adaptive adjust the structural information to be preserved. With an effective loss function to improve the visual quality of the generated font images, we show the effectiveness of the proposed MF-Net based on quantitative and subjective visual evaluation, and compare it with the existing models in the scenario of stylized multilingual font generation. The source code is available on https://github.com/iamyufan/MF-Net.},
   booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
   pages = {2088–2096},
   numpages = {9},
   keywords = {font design, image synthesis, few-shot learning, style transfer},
   location = {Lisboa, Portugal},
   series = {MM '22}
}

감사의 말

FRTRANSGAN에서 파생 및 재구성 된 코드.