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.

我们提出了一个基于深度学习的简单的端到端模型，该模型通过解开视觉表示自动将草绘的对象分解为组件。随着类别的增加，基于视觉表示学习的模型的性能会下降。与其建立从静态图像到整个草图序列的映射，我们提出草图的可解释的解开表示来理解组件概念以及这些概念之间的关系。我们的模型获取草图对象的二进制图像，并生成与草图中的关键零部件相对应的零部件笔划序列集。实验表明，在解缠程度上，我们的方法在数量上显着优于所有基线，并且在数十种类别上训练时，我们的方法更加稳定。