Paper Info
Title
Data-Driven Sketch Beautification With Neural Feature Representation
Abstract
This article 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 article, 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 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.
Year
DOI
Venue
2022
10.1109/MCG.2021.3115181
IEEE Computer Graphics and Applications
Keywords
DocType
Volume
Algorithms,Art,Neural Networks, Computer,Semantics
Journal
42
Issue
ISSN
Citations 
4
0272-1716
0
PageRank 
References 
Authors
0.34
11
1
Name
Order
Citations
PageRank
I-Chao Shen110913.17