A Neural Representation of Sketch Drawings

Anonymous

Nov 03, 2017 (modified: Nov 03, 2017) ICLR 2018 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: 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 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.
  • TL;DR: We investigate alternative to traditional pixel image modelling approaches, and propose a generative model for vector images.
  • Keywords: applications, image modelling, computer-assisted, drawing, art, creativity, dataset

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