DCR: Disentangled component representation for sketch generation *

We present a simple end-to-end model based on deep learning to automatically decompose sketched objects into components by disentangling the visual representation. The performance of visual representation learning based models degrades as categories increase. Rather than building a mapping from a static image to the whole sketch sequences, we propose an interpretable disentangled representation of sketch to understand component concepts and the relationship among such concepts. Our model takes the binary image of a sketched object and produces a component stroke sequence set corresponding to key components in the sketch. Experiments show that our method significantly outperforms all baselines quantitatively at the degree of disentanglement, and our method is more stable while training on tens of categories. (c) 2021 Published by Elsevier B.V.