Despite the ubiquitous use of materials maps in modern rendering pipelines, their editing
and control remains a challenge. In this paper, we present an example‐based material …

In this paper we present Diffusion Image Analogies—an example-based image editing
approach that builds upon the concept of image analogies originally introduced by …

Synthetic images created by generative models increase in quality and expressiveness as
newer models utilize larger datasets and novel architectures. Although this photorealism is a …

Arbitrary style transfer has broad application prospects and important research values, and it
is a research hotspot in the field of computer vision. Many studies have shown that arbitrary …

We present a deep learning-based method for propagating spatially-varying visual material
attributes (eg, texture maps or image stylizations) to larger samples of the same or similar …

Digital painting interfaces require an input fidelity that preserves the artistic expression of the
user. Drawing tablets allow for precise and low-latency sensing of pen motions and other …

Text-to-video (T2V) models have shown remarkable capabilities in generating diverse
videos. However, they struggle to produce user-desired artistic videos due to (i) text's …

The field of neural style transfer refers to the re-rendering of content image while fusing the
features of a style image. The recent studies either focus on multiple style transfer or …

With the development of image style transfer technologies, portrait style transfer has
attracted growing attention in this research community. In this article, we present an …

Existing CNN-based style transfer frameworks have suffered from inaccurate control of pixel-
wise stylization. As the CNN operation is designed based on kernel-wise operation, such a …