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cs.CV2020
ActGAN: Flexible and Efficient One-shot Face Reenactment
Ivan Kosarevych, Marian Petruk, Markian Kostiv +3
This paper introduces ActGAN - a novel end-to-end generative adversarial network (GAN) for one-shot face reenactment. Given two images, the goal is to transfer the facial expressio…
cs.CV2017
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych +2
We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning is based on a conditional GAN and the content loss . DeblurGAN achieves state-of-the art perf…