12 citations · 14 across the 4 of their papers we have counts for
6 papers
Neural Neighbor Style Transfer
Nicholas Kolkin, Michal Kucera, Sylvain Paris +3
We propose Neural Neighbor Style Transfer (NNST), a pipeline that offers state-of-the-art quality, generalization, and competitive efficiency for artistic style transfer. Our appro…
STALP: Style Transfer with Auxiliary Limited Pairing
David Futschik, Michal Kučera, Michal Lukáč +3
We present an approach to example-based stylization of images that uses a single pair of a source image and its stylized counterpart. We demonstrate how to train an image translati…
Real Image Inversion via Segments
David Futschik, Michal Lukáč, Eli Shechtman +1
In this short report, we present a simple, yet effective approach to editing real images via generative adversarial networks (GAN). Unlike previous techniques, that treat all editi…
Interactive Video Stylization Using Few-Shot Patch-Based Training
Ondřej Texler, David Futschik, Michal Kučera +5
In this paper, we present a learning-based method to the keyframe-based video stylization that allows an artist to propagate the style from a few selected keyframes to the rest of…
StyleBlit: Fast Example-Based Stylization with Local Guidance
Daniel Sýkora, Ondřej Jamriška, Jingwan Lu +1
We present StyleBlit---an efficient example-based style transfer algorithm that can deliver high-quality stylized renderings in real-time on a single-core CPU. Our technique is esp…
Building Anatomically Realistic Jaw Kinematics Model from Data
Wenwu Yang, Nathan Marshak, Daniel Sýkora +2
This paper considers a different aspect of anatomical face modeling: kinematic modeling of the jaw, i.e., the Temporo-Mandibular Joint (TMJ). Previous work often relies on simple m…