ClipGen: A Deep Generative Model for Clipart Vectorization and Synthesis

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.

15 pages, TVCG2021

References in corpus (7)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Conditional Generative Adversarial Nets
• Fully Convolutional Networks for Semantic Segmentation
• Comparative Study of CNN and RNN for Natural Language Processing
• A Neural Representation of Sketch Drawings
• Semantic Photo Manipulation with a Generative Image Prior
• RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud