Abstract Neural Style Transfer (NST) was originally proposed to use feature extraction
capabilities of Neural Networks as a way to perform Style Transfer with images. Pre-trained …

Neural Style Transfer (NST) refers to a class of algorithms able to manipulate an element,
most often images, to adopt the appearance or style of another one. Each element is defined …

In the fourth industrial revolution, the scale of execution for interactive applications increased
substantially. These interactive and animated applications are human-centric, and the …

We report on our approach towards creating socially intelligent robots, which is heavily
inspired by recent experimental findings about the neurocognitive mechanisms underlying …

With the rapid development of computer computing power, as an important method in the
field of artificial intelligence, deep learning has amazing learning ability, especially in …

We propose a mechanism to communicate emotions to humans by using head, torso and
arm movements of a humanoid robot, without exploiting its facial features. To this end, we …

This work presents CineTransfer, an algorithmic framework that drives a robot to record a
video sequence that mimics the cinematographic style of an input video. We propose …

Traditional methods of style transfer emphasize primarily the transfer of artistic styles. In
recent years, style transfer has expanded beyond the realm of artistic expression to …

We propose StyleBank, which is composed of multiple convolution filter banks and each
filter bank explicitly represents one style, for neural image style transfer. To transfer an …

In this paper we propose a noval method to generate the specified network parameters
through one feed-forward propagation in the meta networks for neural style transfer. Recent …