AI Based Font Pair Suggestion Modelling For Graphic Design

• URL: http://arxiv.org/abs/2501.10969v1
• Date: Sun, 19 Jan 2025 07:08:36 GMT
• Title: AI Based Font Pair Suggestion Modelling For Graphic Design
• Authors: Aryan Singh, Sumithra Bhakthavatsalam,
• Abstract summary: One of the key challenges of AI generated designs in Microsoft Designer is selecting the most contextually relevant and novel fonts for the design suggestions. In this work we create font visual embeddings, a font stroke width algorithm, a font category to font mapping dataset and a lightweight, low latency knowledge-distilled mini language model (Mini LM V2) to recommend multiple pairs of contextual heading and subheading fonts.
• Score: 0.0
• License:
• Abstract: One of the key challenges of AI generated designs in Microsoft Designer is selecting the most contextually relevant and novel fonts for the design suggestions. Previous efforts involved manually mapping design intent to fonts. Though this was high quality, this method does not scale for a large number of fonts (3000+) and numerous user intents for graphic design. In this work we create font visual embeddings, a font stroke width algorithm, a font category to font mapping dataset, an LLM-based category utilization description and a lightweight, low latency knowledge-distilled mini language model (Mini LM V2) to recommend multiple pairs of contextual heading and subheading fonts for beautiful and intuitive designs. We also utilize a weighted scoring mechanism, nearest neighbor approach and stratified sampling to rank the font pairs and bring novelty to the predictions.

Related papers

• GLDesigner: Leveraging Multi-Modal LLMs as Designer for Enhanced Aesthetic Text Glyph Layouts [53.568057283934714]
  We propose a VLM-based framework that generates content-aware text logo layouts. We introduce two model techniques to reduce the computation for processing multiple glyph images simultaneously. To support instruction-tuning of out model, we construct two extensive text logo datasets, which are 5x more larger than the existing public dataset.
  arXiv  Detail & Related papers  (2024-11-18T10:04:10Z)
• TextLap: Customizing Language Models for Text-to-Layout Planning [65.02105936609021]
  We call our method TextLap (text-based layout planning) It uses a curated instruction-based layout planning dataset (InsLap) to customize Large language models (LLMs) as a graphic designer. We demonstrate the effectiveness of TextLap and show that it outperforms strong baselines, including GPT-4 based methods, for image generation and graphical design benchmarks.
  arXiv  Detail & Related papers  (2024-10-09T19:51:38Z)
• Automatic Layout Planning for Visually-Rich Documents with Instruction-Following Models [81.6240188672294]
  In graphic design, non-professional users often struggle to create visually appealing layouts due to limited skills and resources. We introduce a novel multimodal instruction-following framework for layout planning, allowing users to easily arrange visual elements into tailored layouts. Our method not only simplifies the design process for non-professionals but also surpasses the performance of few-shot GPT-4V models, with mIoU higher by 12% on Crello.
  arXiv  Detail & Related papers  (2024-04-23T17:58:33Z)
• VQ-Font: Few-Shot Font Generation with Structure-Aware Enhancement and Quantization [52.870638830417]
  We propose a VQGAN-based framework (i.e., VQ-Font) to enhance glyph fidelity through token prior refinement and structure-aware enhancement. Specifically, we pre-train a VQGAN to encapsulate font token prior within a codebook. Subsequently, VQ-Font refines the synthesized glyphs with the codebook to eliminate the domain gap between synthesized and real-world strokes.
  arXiv  Detail & Related papers  (2023-08-27T06:32:20Z)
• VecFontSDF: Learning to Reconstruct and Synthesize High-quality Vector Fonts via Signed Distance Functions [15.47282857047361]
  This paper proposes an end-to-end trainable method, VecFontSDF, to reconstruct and synthesize high-quality vector fonts. Based on the proposed SDF-based implicit shape representation, VecFontSDF learns to model each glyph as shape primitives enclosed by several parabolic curves.
  arXiv  Detail & Related papers  (2023-03-22T16:14:39Z)
• Diff-Font: Diffusion Model for Robust One-Shot Font Generation [110.45944936952309]
  We propose a novel one-shot font generation method based on a diffusion model, named Diff-Font. The proposed model aims to generate the entire font library by giving only one sample as the reference. The well-trained Diff-Font is not only robust to font gap and font variation, but also achieved promising performance on difficult character generation.
  arXiv  Detail & Related papers  (2022-12-12T13:51:50Z)
• Font Representation Learning via Paired-glyph Matching [15.358456947574913]
  We propose a novel font representation learning scheme to embed font styles into the latent space. For the discriminative representation of a font from others, we propose a paired-glyph matching-based font representation learning model. We show our font representation learning scheme achieves better generalization performance than the existing font representation learning techniques.
  arXiv  Detail & Related papers  (2022-11-20T12:27:27Z)
• FontNet: Closing the gap to font designer performance in font synthesis [3.991334489146843]
  We propose a model, called FontNet, that learns to separate font styles in the embedding space where distances directly correspond to a measure of font similarity. We design the network architecture and training procedure that can be adopted for any language system and can produce high-resolution font images.
  arXiv  Detail & Related papers  (2022-05-13T08:37:10Z)
• Scalable Font Reconstruction with Dual Latent Manifolds [55.29525824849242]
  We propose a deep generative model that performs typography analysis and font reconstruction. Our approach enables us to massively scale up the number of character types we can effectively model. We evaluate on the task of font reconstruction over various datasets representing character types of many languages.
  arXiv  Detail & Related papers  (2021-09-10T20:37:43Z)
• Few-shot Compositional Font Generation with Dual Memory [16.967987801167514]
  We propose a novel font generation framework, named Dual Memory-augmented Font Generation Network (DM-Font) We employ memory components and global-context awareness in the generator to take advantage of the compositionality. In the experiments on Korean-handwriting fonts and Thai-printing fonts, we observe that our method generates a significantly better quality of samples with faithful stylization.
  arXiv  Detail & Related papers  (2020-05-21T08:13:40Z)
• Neural Style Difference Transfer and Its Application to Font Generation [14.567067583556717]
  We will introduce a method to create fonts automatically. The difference of font styles between two different fonts is found and transferred to another font using neural style transfer.
  arXiv  Detail & Related papers  (2020-01-21T03:32:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.