A Neural Representation of Sketch Drawings

04/11/2017
by   David Ha, et al.
0

We present sketch-rnn, a recurrent neural network (RNN) able to construct stroke-based drawings of common objects. The model is trained on thousands of crude human-drawn images representing hundreds of 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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/26/2021

SketchLattice: Latticed Representation for Sketch Manipulation

The key challenge in designing a sketch representation lies with handlin...
research
09/13/2017

Sketch-pix2seq: a Model to Generate Sketches of Multiple Categories

Sketch is an important media for human to communicate ideas, which refle...
research
11/20/2018

Sketch-R2CNN: An Attentive Network for Vector Sketch Recognition

Freehand sketching is a dynamic process where points are sequentially sa...
research
11/09/2020

Sketch-Inspector: a Deep Mixture Model for High-Quality Sketch Generation of Cats

With the involvement of artificial intelligence (AI), sketches can be au...
research
08/11/2016

Enabling My Robot To Play Pictionary : Recurrent Neural Networks For Sketch Recognition

Freehand sketching is an inherently sequential process. Yet, most approa...
research
08/27/2020

SketchEmbedNet: Learning Novel Concepts by Imitating Drawings

Sketch drawings are an intuitive visual domain that appeals to human ins...
research
11/15/2018

Sketch based Reduced Memory Hough Transform

This paper proposes using sketch algorithms to represent the votes in Ho...

Please sign up or login with your details

Forgot password? Click here to reset