42 citations · 179 across the 23 of their papers we have counts for
37 papers · 1 filter
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…
Collaging Class-specific GANs for Semantic Image Synthesis
Yuheng Li, Yijun Li, Jingwan Lu +3
We propose a new approach for high resolution semantic image synthesis. It consists of one base image generator and multiple class-specific generators. The base generator generates…
Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN
Badour AlBahar, Jingwan Lu, Jimei Yang +3
We present an algorithm for re-rendering a person from a single image under arbitrary poses. Existing methods often have difficulties in hallucinating occluded contents photo-reali…
Ensembling with Deep Generative Views
Lucy Chai, Jun-Yan Zhu, Eli Shechtman +2
Recent generative models can synthesize "views" of artificial images that mimic real-world variations, such as changes in color or pose, simply by learning from unlabeled image col…
Few-shot Image Generation via Cross-domain Correspondence
Utkarsh Ojha, Yijun Li, Jingwan Lu +4
Training generative models, such as GANs, on a target domain containing limited examples (e.g., 10) can easily result in overfitting. In this work, we seek to utilize a large sourc…