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Further academic publications

This section aims to:

1. provide a collection of further relevant academic papers

2. explain them in detail

3. ideally reproduce the results and discuss these critically

In order to be relevant, papers need to discuss the use of Deep Learning in combination with vector graphics, such as SVG or other stroke-based graphics.

Papers that may be relevant but are not discussed include:

• Vectorization

  • Video Vectorization via Bipartite Diffusion Curves Propagation and Optimization (2021)

  • Deep Vectorization of Technical Drawings (2020)

• Text-to-vector-drawing synthesis / clipart synthesis

  • StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Translation (2022)

    • The drawings are of relatively poor quality / low fidelity

  • Paint Transformer: Feed Forward Neural Painting with Stroke Prediction (2021)

  • ClipGen: A Deep Generative Model for Clipart Vectorization and Synthesis (2021)

    • Note: Should become a key paper and discussed…

  • CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders (2021)

    • The drawings are of relatively poor quality / low fidelity

  • A Multi-Implicit Neural Representation for Fonts (2021) by Reddy et al.

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Jain et al. (2022) | VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models

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Campbell and Kautz (2014) | Learning a Manifold of Fonts