ClipGen: A Deep Generative Model for Clipart Vectorization and Synthesis

TL;DR

This paper introduces ClipGen, a deep generative model that automatically vectorizes and synthesizes clipart images of man-made objects, enabling efficient and intuitive clipart creation.

Contribution

It presents a novel iterative generative model for clipart vectorization and synthesis, including a new dataset and preprocessing tasks for improved results.

Findings

Successfully vectorized diverse clipart categories.

Generated high-quality, category-recognizable clipart.

Demonstrated potential for aiding clipart design workflows.

Abstract

This paper presents a novel deep learning-based approach for automatically vectorizing and synthesizing the clipart of man-made objects. Given a raster clipart image and its corresponding object category (e.g., airplanes), the proposed method sequentially generates new layers, each of which is composed of a new closed path filled with a single color. The final result is obtained by compositing all layers together into a vector clipart image that falls into the target category. The proposed approach is based on an iterative generative model that (i) decides whether to continue synthesizing a new layer and (ii) determines the geometry and appearance of the new layer. We formulated a joint loss function for training our generative model, including the shape similarity, symmetry, and local curve smoothness losses, as well as vector graphics rendering accuracy loss for synthesizing clipart recognizable by humans. We also introduced a collection of man-made object clipart, ClipNet, which is composed of closed-path layers, and two designed preprocessing tasks to clean up and enrich the original raw clipart. To validate the proposed approach, we conducted several experiments and demonstrated its ability to vectorize and synthesize various clipart categories. We envision that our generative model can facilitate efficient and intuitive clipart designs for novice users and graphic designers.