Papers Font Generation
“Font Generation” 태그가 달린 논문 52편 · 필터 해제
MX-Font++: Mixture of Heterogeneous Aggregation Experts for Few-shot Font Generation
Few-shot Font Generation (FFG) aims to create new font libraries using limited reference glyphs, with crucial applications in digital accessibility and equity for low-resource languages, especially in multilingual artifi…
Font GenerationMixture-of-ExpertsSkeleton and Font Generation Network for Zero-shot Chinese Character Generation
Automatic font generation remains a challenging research issue, primarily due to the vast number of Chinese characters, each with unique and intricate structures. Our investigation of previous studies reveals inherent bi…
Data AugmentationFont GenerationFont-Agent: Enhancing Font Understanding with Large Language Models
The rapid development of generative models has significantly advanced font generation. However, limited exploration has been devoted to the evaluation and interpretability of graphical fonts. Existing quality assessm…
Font GenerationQuestion AnsweringOne-Shot Multilingual Font Generation Via ViT
Font design poses unique challenges for logographic languages like Chinese, Japanese, and Korean (CJK), where thousands of unique characters must be individually crafted. This paper introduces a novel Vision Transformer …
Font GenerationRAGRetrievalGRIF-DM: Generation of Rich Impression Fonts using Diffusion Models
Fonts are integral to creative endeavors, design processes, and artistic productions. The appropriate selection of a font can significantly enhance artwork and endow advertisements with a higher level of expressivity. De…
DescriptiveFont GenerationEfficient and Scalable Chinese Vector Font Generation via Component Composition
Chinese vector font generation is challenging due to the complex structure and huge amount of Chinese characters. Recent advances remain limited to generating a small set of characters with simple structure. In this work…
Font GenerationDesign and Development of a Framework For Stroke-Based Handwritten Gujarati Font Generation
Handwritten font generation is important for preserving cultural heritage and creating personalized designs. It adds an authentic and expressive touch to printed materials, making them visually appealing and establishing…
Font GenerationOptical Character Recognition (OCR)Generate Like Experts: Multi-Stage Font Generation by Incorporating Font Transfer Process into Diffusion Models
Few-shot font generation (FFG) produces stylized font images with a limited number of reference samples which can significantly reduce labor costs in manual font designs. Most existing FFG methods follow the style-co…
DisentanglementFont GenerationGenerative Adversarial NetworkFontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning
Automatic font generation is an imitation task, which aims to create a font library that mimics the style of reference images while preserving the content from source images. Although existing font generation methods hav…
Contrastive LearningDenoisingFont GenerationFont Style Transfer+2VecFusion: Vector Font Generation with Diffusion
We present VecFusion, a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions. Our approach is a cascaded diffusion model which consists of a raste…
Font GenerationVector GraphicsDeepCalliFont: Few-shot Chinese Calligraphy Font Synthesis by Integrating Dual-modality Generative Models
Few-shot font generation, especially for Chinese calligraphy fonts, is a challenging and ongoing problem. With the help of prior knowledge that is mainly based on glyph consistency assumptions, some recently proposed met…
Font GenerationImage GenerationRepresentation LearningLocal Style Awareness of Font Images
When we compare fonts, we often pay attention to styles of local parts, such as serifs and curvatures. This paper proposes an attention mechanism to find important local parts. The local parts with larger attention are t…
Font GenerationFew shot font generation via transferring similarity guided global style and quantization local style
Automatic few-shot font generation (AFFG), aiming at generating new fonts with only a few glyph references, reduces the labor cost of manually designing fonts. However, the traditional AFFG paradigm of style-content dise…
DisentanglementFont GenerationQuantizationStyle TransferVQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization
Few-shot font generation is challenging, as it needs to capture the fine-grained stroke styles from a limited set of reference glyphs, and then transfer to other characters, which are expected to have similar styles. How…
Font GenerationQuantizationCF-Font: Content Fusion for Few-shot Font Generation
Content and style disentanglement is an effective way to achieve few-shot font generation. It allows to transfer the style of the font image in a source domain to the style defined with a few reference images in a target…
DisentanglementFont GenerationFew-shot Font Generation by Learning Style Difference and Similarity
Few-shot font generation (FFG) aims to preserve the underlying global structure of the original character while generating target fonts by referring to a few samples. It has been applied to font library creation, a perso…
Contrastive LearningFont GenerationNeural Transformation Fields for Arbitrary-Styled Font Generation
Few-shot font generation (FFG), aiming at generating font images with a few samples, is an emerging topic in recent years due to the academic and commercial values. Typically, the FFG approaches follow the style-cont…
DisentanglementFont GenerationJoint Implicit Neural Representation for High-fidelity and Compact Vector Fonts
Existing vector font generation approaches either struggle to preserve high-frequency corner details of the glyph or produce vector shapes that have redundant segments, which hinders their applications in practical s…
Font GenerationDGFont++: Robust Deformable Generative Networks for Unsupervised Font Generation
Automatic font generation without human experts is a practical and significant problem, especially for some languages that consist of a large number of characters. Existing methods for font generation are often in superv…
Font GenerationImage-to-Image TranslationSelf-Supervised LearningUnsupervised Image-To-Image TranslationDiff-Font: Diffusion Model for Robust One-Shot Font Generation
Font generation is a difficult and time-consuming task, especially in those languages using ideograms that have complicated structures with a large number of characters, such as Chinese. To solve this problem, few-shot f…
Font Generation