This paper presents a data-driven approach for beautifying freehand sketches. Our key premise is that the artist-drawn vector can be used to sketch visually appealing shapes, such as local shapes with a clean appearance and better global visual properties (e.g., symmetry). However, these merits may not apply to all object categories. In this paper, we use a neural network to represent local and global merits across different object categories, to design our beautification method. First, we match sample points between input sketches and the collected vector shapes using the extracted feature representations. Then, we then design an optimization problem to ensure resemblance between the deformed sketch and vector shape in the representation space, while preserving the semantic meaning and style of the original sketch. Finally, we demonstrate our method on sketches across different shape categories.

I thank the anonymous reviewers who helped me improve the paper. This study was supported by the MediaTek Fellowship and JSPS KAKENHI Grant Number JP21F20075.

@article{ shen2021ddsb,	      
author    = {I-Chao Shen},
title     = {Data-driven Sketch Beautification with Neural Feature Representation,
journal   = {Computer Graphics and Applications (CG&A)},
year      = {2021}
}