A Neural Representation of Sketch Drawings
This work addresses the challenge of automated sketch generation for applications in creative AI and human-computer interaction, representing an incremental advancement in neural network-based drawing models.
The authors tackled the problem of generating stroke-based drawings of common objects by introducing sketch-rnn, a recurrent neural network trained on thousands of crude human-drawn images across hundreds of classes, resulting in a framework for conditional and unconditional sketch generation with robust training methods for coherent vector drawings.
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.