Sketching is more fundamental to human cognition than speech. Deep Neural Networks (DNNs) have achieved the state-of-the-art in speech-related tasks but have not made significant development in generating stroke-based sketches a.k.a sketches in vector format. Though there are Variational Auto Encoders (VAEs) for generating sketches in vector format, there is no Generative Adversarial Network (GAN) architecture for the same. In this paper, we propose a standalone GAN architecture SkeGAN and a VAE-GAN architecture VASkeGAN, for sketch generation in vector format. SkeGAN is a stochastic policy in Reinforcement Learning (RL), capable of generating both multidimensional continuous and discrete outputs. VASkeGAN hybridizes a VAE and a GAN, in order to couple the efficient representation of data by VAE with the powerful generating capabilities of a GAN, to produce visually appealing sketches. We also propose a new metric called the Ske-score which quantifies the quality of vector sketches. We have validated that SkeGAN and VASkeGAN generate visually appealing sketches by using Human Turing Test and Ske-score.
All the architectures mentioned for sketch generation in vector format, are VAEs. A well known disadvantage with VAEs, they tend to produce blurred images in case of raster images. Since there is no concept of blurring, the vector images produced by VAEs like sketch-rnn @cite_10 tend to suffer from a mode-collapse-like situation wherein the pen is not lifted to draw at another location, but stays on the paper and continues to scribble. We call this as the . Figure shows this effect in the sketches of yoga poses" and mosquitos". Since VAEs assume the prior to be Gaussian, they need to be trained for a very large number of iterations so that the weights of the decoder get adjusted accordingly in order to generate close to the distribution of data. In the case of @cite_10 , the training is done for 10 million iterations. Also, GANs have performed outstandingly well for a variety of tasks mentioned in Section , with raster images. In order to alleviate these disadvantages of VAEs and harness the power of GANs, we propose a standalone GAN called the and another GAN called the with which we compare SkeGAN.
@cite_10
2606712314
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.