Keypoint-Driven Line Drawing Vectorization via PolyVector Flow

Ivan Puhachov¹, William Neveu¹, Edward Chien², Mikhail Bessmeltsev¹

¹ Université de Montréal
² Boston University

Code (TBD)
Dataset (TBD)

Responsive image
Line drawing vectorization is a daily task in graphic design, computer animation, and engineering, necessary to convert raster images to a set of curves for editing and geometry processing. Despite recent progress in the area, automatic vectorization tools often produce spurious branches or incorrect connectivity around curve junctions; or smooth out sharp corners. These issues detract from the use of such vectorization tools, both from an aesthetic viewpoint and for feasibility of downstream applications (e.g., automatic coloring or inbetweening). We address these problems by introducing a novel line drawing vectorization algorithm that splits the task into three components: (1) finding keypoints, i.e., curve endpoints, junctions, and sharp corners; (2) extracting drawing topology, i.e., finding connections between keypoints; and (3) computing the geometry of those connections. We compute the optimal geometry of the connecting curves via a novel geometric flow — PolyVector Flow — that aligns the curves to the drawing, disambiguating directions around Y-, X-, and T-junctions. We show that our system robustly infers both the geometry and topology of detailed complex drawings. We validate our system both quantitatively and qualitatively, demonstrating that our method visually outperforms previous work.
author = {Ivan Puhachov and William Neveu and Edward Chien and Mikhail Bessmeltsev},
title = {Keypoint-Driven Line Drawing Vectorization via PolyVector Flow},
journal = {ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia)},
volume = {40}, number = {6}, year = {2021}, month = dec,
doi = {10.1145/3478513.3480529}