AutoDraw: Fast Drawing For Everyone.

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Link: AutoDraw: Fast drawing for everyone

 

[Arthur]

Related Google blog post...

 

Teaching Machines to Draw

 

Abstract visual communication is a key part of how people convey ideas to one another. From a young age, children develop the ability to depict objects, and arguably even emotions, with only a few pen strokes. These simple drawings may not resemble reality as captured by a photograph, but they do tell us something about how people represent and reconstruct images of the world around them.

 

In our recent paper, “A Neural Representation of Sketch Drawings”, we present a generative recurrent neural network capable of producing sketches of common objects, with the goal of training a machine to draw and generalize abstract concepts in a manner similar to humans. We train our model on a dataset of hand-drawn sketches, each represented as a sequence of motor actions controlling a pen: which direction to move, when to lift the pen up, and when to stop drawing. In doing so, we created a model that potentially has many applications, from assisting the creative process of an artist, to helping teach students how to draw.

 

In this work, we investigate a lower-dimensional vector-based representation inspired by how people draw. Our model, sketch-rnn, is based on the sequence-to-sequence (seq2seq) autoencoder framework. It incorporates variational inference and utilizes hypernetworks as recurrent neural network cells. The goal of a seq2seq autoencoder is to train a network to encode an input sequence into a vector of floating point numbers, called a latent vector, and from this latent vector reconstruct an output sequence using a decoder that replicates the input sequence as closely as possible.