We present sketch-rnn, a recurrent neural network able to construct stroke-based drawings of common objects. The model is trained on a dataset of human-drawn images representing many different classes. We outline a framework for conditional and unconditional sketch generation, and describe new robust training methods for generating coherent sketch drawings in a vector format.
We investigate alternative to traditional pixel image modelling approaches, and propose a generative model for vector images. This paper introduces a neural network architecture for generating sketch drawings inspired by the variational autoencoder.