DreamFont3D: Personalized Text-to-3D Artistic Font Generation


Xiang Li1, Lei Meng1,2, Lei Wu1, Manyi Li1, Xiangxu Meng1,

1Shandong University   2Shandong Research Institute of Industrial Technology    

Abstract


This paper presents a novel text-to-3D font generation model, which achieves stunning 3D representation of artistic font and the control of local effects.
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Text-to-3D artistic font generation aims to assist users for innovative and customized 3D font design by exploring novel concepts and styles. Despite of the advances in the text-to-3D tasks for general objects or scenes, the additional challenge of 3D font generation is to preserve the geometric structures of strokes in an appropriate extent, which determines the generation quality in terms of the recognizability and the local effect control of the 3D fonts. This paper presents a novel approach for text-to-3D artistic font generation, named DreamFont3D, which utilizes multi-view font masks and layout conditions to constrain the 3D font structure and local font effects. Specifically, to enhance the recognizability of 3D fonts, we propose the multi-view mask constraint (MC) to optimize the differentiable 3D representation while preserving the font structure. We also present a progressive mask weighting (MW) module to ensure a trade-off between the text-guided stylization of font effects and the mask-guided preservation of font structure. For precise control over local font effects, we design the multi-view attention modulation (AM) that guides the visual concepts to appear in specific regions according to the provided layout conditions. Compared with existing text-to-3D methods, DreamFont3D shows its own superiority in the consistency between font effects and text prompts, the recognizability, and the localization of font effects.


Overview of the DreamFont3D


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The Results with and without layout conditions


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The Results of different font glyph


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More results


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Citation


@article{dreamfont3d2024,
  title={DreamFont3D: Personalized Text-to-3D Artistic Font Generation},
  author={Xiang Li, Lei Meng, Lei Wu, Manyi Li, Xiangxu Meng.},
  journal={},
  year={2024}
}