The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks
(CNNs) in creating artistic imagery by separating and recombining image content and style …

We introduce a new approach to tone management for photographs. Whereas traditional
tone-map** operators target a neutral and faithful rendition of the input image, we explore …

Video sequences contain many cues that may be used to segment objects in them, such as
color, gradient, color adjacency, shape, temporal coherence, camera and object motion, and …

Makeup transfer (MT) aims to transfer the makeup style from a given reference makeup face
image to a source image while preserving face identity and background information. In …

We perform co-analysis of a set of man-made 3D objects to allow the creation of novel
instances derived from the set. We analyze the objects at the part level and treat the …

Patch-based texture synthesis algorithms produce reasonable results for a wide variety of
texture classes. They preserve global structure, but often introduce unwanted visual artifacts …

Arbitrary neural style transfer has been a popular research topic due to its rich application
scenarios. Effective disentanglement of content and style is the critical factor for synthesizing …

The Science of Deep Learning emerged from courses taught by the author that have
provided thousands of students with training and experience for their academic studies, and …

Example-based stylization provides an easy way of making artistic effects for images and
videos. However, most existing methods do not consider the content and style separately. In …