A Neural Representation of Sketch Drawings

We present sketch-rnn, a recurrent neural network (RNN) 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 demonstrate that our representation is useful for computer-aided artistic work such as conditional generation, generating multiple outcomes from a partial drawing, and morphing a drawing from one class to another.