12 papers · 1 filter
Type-R: Automatically Retouching Typos for Text-to-Image Generation
Wataru Shimoda, Naoto Inoue, Daichi Haraguchi +3
While recent text-to-image models can generate photorealistic images from text prompts that reflect detailed instructions, they still face significant challenges in accurately rend…
Can GPTs Evaluate Graphic Design Based on Design Principles?
Daichi Haraguchi, Naoto Inoue, Wataru Shimoda +3
Recent advancements in foundation models show promising capability in graphic design generation. Several studies have started employing Large Multimodal Models (LMMs) to evaluate g…
Singularities in bivariate normal mixtures
Yutaro Kabata, Hirotaka Matsumoto, Seiichi Uchida +1
We investigate mappings where are bivariate normal densities from the perspective of singularity theory of mappi…
Cross-Domain Image Conversion by CycleDM
Sho Shimotsumagari, Shumpei Takezaki, Daichi Haraguchi +1
The purpose of this paper is to enable the conversion between machine-printed character images (i.e., font images) and handwritten character images through machine learning. For th…
Total Disentanglement of Font Images into Style and Character Class Features
Daichi Haraguchi, Wataru Shimoda, Kota Yamaguchi +1
In this paper, we demonstrate a total disentanglement of font images. Total disentanglement is a neural network-based method for decomposing each font image nonlinearly and complet…
An Ordinal Diffusion Model for Generating Medical Images with Different Severity Levels
Shumpei Takezaki, Seiichi Uchida
Diffusion models have recently been used for medical image generation because of their high image quality. In this study, we focus on generating medical images with ordinal classes…
What Text Design Characterizes Book Genres?
Daichi Haraguchi, Brian Kenji Iwana, Seiichi Uchida
This study analyzes the relationship between non-verbal information (e.g., genres) and text design (e.g., font style, character color, etc.) through the classification of book genr…
Impression-CLIP: Contrastive Shape-Impression Embedding for Fonts
Yugo Kubota, Daichi Haraguchi, Seiichi Uchida
Fonts convey different impressions to readers. These impressions often come from the font shapes. However, the correlation between fonts and their impression is weak and unstable b…
Font Impression Estimation in the Wild
Kazuki Kitajima, Daichi Haraguchi, Seiichi Uchida
This paper addresses the challenging task of estimating font impressions from real font images. We use a font dataset with annotation about font impressions and a convolutional neu…
Typographic Text Generation with Off-the-Shelf Diffusion Model
KhayTze Peong, Seiichi Uchida, Daichi Haraguchi
Recent diffusion-based generative models show promise in their ability to generate text images, but limitations in specifying the styles of the generated texts render them insuffic…
Font Style Interpolation with Diffusion Models
Tetta Kondo, Shumpei Takezaki, Daichi Haraguchi +1
Fonts have huge variations in their styles and give readers different impressions. Therefore, generating new fonts is worthy of giving new impressions to readers. In this paper, we…
Learning to Kern: Set-wise Estimation of Optimal Letter Space
Kei Nakatsuru, Seiichi Uchida
Kerning is the task of setting appropriate horizontal spaces for all possible letter pairs of a certain font. One of the difficulties of kerning is that the appropriate space diffe…