FontCrafter: High-Fidelity Element-Driven Artistic Font Creation with Visual In-Context Generation
Not on the map. Why?
Abstract (arXiv)
Artistic font generation aims to synthesize stylized glyphs based on a reference style. However, existing approaches suffer from limited style diversity and coarse control. In this work, we explore the potential of element-driven artistic font generation. Elements are the fundamental visual units of a font, serving as reference images for the desired style. Conceptually, we categorize elements into object elements (e.g., flowers or stones) with distinct structures and amorphous elements (e.g., flames or clouds) with unstructured textures. We introduce FontCrafter, an element-driven framework for font creation, and construct a large-scale dataset, ElementFont, which contains diverse element types and high-quality glyph images. However, achieving high-fidelity reconstruction of both texture and structure of reference elements remains challenging. To address this, we propose an in-context generation strategy that treats element images as visual context and uses an inpainting model to transfer element styles into glyph regions at the pixel level. To further control glyph shapes, we design a lightweight Context-aware Mask Adapter (CMA) that injects shape information. Moreover, a training-free attention redirection mechanism enables region-aware style control and suppresses stroke hallucination. In addition, edge repainting is applied to make boundaries more natural. Extensive experiments demonstrate that FontCrafter achieves strong zero-shot generation performance, particularly in preserving structural and textural fidelity, while also supporting flexible controls such as style mixture.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 46 of its 63 references that are in the map. First posted on arXiv 23 Mar 2026.
Builds on
46 of 63 references.
References · 46
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2022Scalable Diffusion Models with Transformerscited by 6,054
- 2022Generative Adversarial Networkscited by 5,627
- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,503
- 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,356
- 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,181
- 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
- 2023IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Modelscited by 1,464
- 2023T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Modelscited by 1,351
- 2022Paint by Example: Exemplar-based Image Editing with Diffusion Modelscited by 489
- 2023Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Modelscited by 359
34 more
- 2025In-Context Edit: Enabling Instructional Image Editing with In-Context Generation in Large Scale Diffusion Transformercited by 204
- 2023UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wildcited by 200
- 2024CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Modelscited by 102
- 2017Multi-Content GAN for Few-Shot Font Style Transfercited by 100
- 2021DG-Font: Deformable Generative Networks for Unsupervised Font Generationcited by 70
- 2024Zero-shot Image Editing with Reference Imitationcited by 65
- 2017Separating Style and Content for Generalized Style Transfercited by 64
- 2023Word-As-Image for Semantic Typographycited by 63
- 2025Hume: Introducing System-2 Thinking in Visual-Language-Action Modelcited by 56
- 2019Artistic Glyph Image Synthesis via One-Stage Few-Shot Learningcited by 53
- 2023FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learningcited by 48
- 2020Few-shot Font Generation with Localized Style Representations and Factorizationcited by 47
- 2019Controllable Artistic Text Style Transfer via Shape-Matching GANcited by 45
- 2022Diff-Font: Diffusion Model for Robust One-Shot Font Generationcited by 40
- 2024FonTS: Text Rendering with Typography and Style Controlscited by 40
- 2017Auto-Encoder Guided GAN for Chinese Calligraphy Synthesiscited by 29
- 2017Learning to Write Stylized Chinese Characters by Reading a Handful of Examplescited by 28
- 2023CF-Font: Content Fusion for Few-shot Font Generationcited by 26
- 2016Awesome Typography: Statistics-Based Text Effects Transfercited by 24
- 2018TET-GAN: Text Effects Transfer via Stylization and Destylizationcited by 19
- 2022XMP-Font: Self-Supervised Cross-Modality Pre-training for Few-Shot Font Generationcited by 18
- 2023WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Modelscited by 15
- 2024OneActor: Consistent Character Generation via Cluster-Conditioned Guidancecited by 15
- 2024SpotActor: Training-Free Layout-Controlled Consistent Image Generationcited by 12
- 2018Context-Aware Text-Based Binary Image Stylization and Synthesiscited by 11
- 2018Pyramid Embedded Generative Adversarial Network for Automated Font Generationcited by 11
- 2023SIEDOB: Semantic Image Editing by Disentangling Object and Backgroundcited by 11
- 2022Context-Consistent Semantic Image Editing with Style-Preserved Modulationcited by 10
- 2023Reference-Guided Large-Scale Face Inpainting with Identity and Texture Controlcited by 10
- 2024FontStudio: Shape-Adaptive Diffusion Model for Coherent and Consistent Font Effect Generationcited by 9
- 2025In-Context Brush: Zero-shot Customized Subject Insertion with Context-Aware Latent Space Manipulationcited by 7
- 2026TAG-MoE: Task-Aware Gating for Unified Generative Mixture-of-Expertscited by 6
- 2026Unified Thinker: A General Reasoning Modular Core for Image Generationcited by 5
- 2026Beyond Pixels: Visual Metaphor Transfer via Schema-Driven Agentic Reasoningcited by 4
Built on by
Citing papers per year: 2026: 1.
1 paper.