mlsanityData to 7 Oct 2026

AnyArtisticGlyph: Multilingual Controllable Artistic Glyph Generation

arXiv 2504.04743 ↗First posted 7 Apr 2025cs.CVcited by 0

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Abstract (arXiv)

Artistic Glyph Image Generation (AGIG) differs from current creativity-focused generation models by offering finely controllable deterministic generation. It transfers the style of a reference image to a source while preserving its content. Although advanced and promising, current methods may reveal flaws when scrutinizing synthesized image details, often producing blurred or incorrect textures, posing a significant challenge. Hence, we introduce AnyArtisticGlyph, a diffusion-based, multilingual controllable artistic glyph generation model. It includes a font fusion and embedding module, which generates latent features for detailed structure creation, and a vision-text fusion and embedding module that uses the CLIP model to encode references and blends them with transformation caption embeddings for seamless global image generation. Moreover, we incorporate a coarse-grained feature-level loss to enhance generation accuracy. Experiments show that it produces natural, detailed artistic glyph images with state-of-the-art performance. Our project will be open-sourced on https://github.com/jiean001/AnyArtisticGlyph to advance text generation technology.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 23 references that are in the map. First posted on arXiv 7 Apr 2025.

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References · 19

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7 more
  1. 2022Diff-Font: Diffusion Model for Robust One-Shot Font Generationcited by 40
  2. 2022Few-Shot Font Generation by Learning Fine-Grained Local Stylescited by 34
  3. 2022Look Closer to Supervise Better: One-Shot Font Generation via Component-Based Discriminatorcited by 32
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