A Neural Representation of Sketch Drawings
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.