# Contour Completion by Transformers and Its Application to Vector Font Data

arXiv: https://arxiv.org/abs/2304.13988 · HTML: https://mlsanity.com/paper/2304.13988/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 20 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019)
- [Transformers in Time Series: A Survey](https://mlsanity.com/paper/2202.07125/) (2022)
- [DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation](https://mlsanity.com/paper/2007.11301/) (2020)
- [Im2Vec: Synthesizing Vector Graphics without Vector Supervision](https://mlsanity.com/paper/2102.02798/) (2021)
- [A Learned Representation for Scalable Vector Graphics](https://mlsanity.com/paper/1904.02632/) (2019)
- [STEFANN: Scene Text Editor using Font Adaptive Neural Network](https://mlsanity.com/paper/1903.01192/) (2019)
- [A Multi-Implicit Neural Representation for Fonts](https://mlsanity.com/paper/2106.06866/) (2021)
- [A Deep Factorization of Style and Structure in Fonts](https://mlsanity.com/paper/1910.00748/) (2019)
- [Scalable Font Reconstruction with Dual Latent Manifolds](https://mlsanity.com/paper/2109.06627/) (2021)
- [TrueType Transformer: Character and Font Style Recognition in Outline Format](https://mlsanity.com/paper/2203.05338/) (2022)

## Built on by (most cited first, 2 of 2)

- [Kinetic Typography Diffusion Model](https://mlsanity.com/paper/2407.10476/) (2024)
- [Stroke Modeling Enables Vectorized Character Generation with Large Vectorized Glyph Model](https://mlsanity.com/paper/2511.11119/) (2025)

## Abstract (arXiv)

In documents and graphics, contours are a popular format to describe specific shapes. For example, in the True Type Font (TTF) file format, contours describe vector outlines of typeface shapes. Each contour is often defined as a sequence of points. In this paper, we tackle the contour completion task. In this task, the input is a contour sequence with missing points, and the output is a generated completed contour. This task is more difficult than image completion because, for images, the missing pixels are indicated. Since there is no such indication in the contour completion task, we must solve the problem of missing part detection and completion simultaneously. We propose a Transformer-based method to solve this problem and show the results of the typeface contour completion.