Browsing by Author "Futschik, David"

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    Real-Time Patch-Based Stylization of Portraits Using Generative Adversarial Network
    (The Eurographics Association, 2019) Futschik, David; Chai, Menglei; Cao, Chen; Ma, Chongyang; Stoliar, Aleksei; Korolev, Sergey; Tulyakov, Sergey; Kučera, Michal; Sýkora, Daniel; Kaplan, Craig S. and Forbes, Angus and DiVerdi, Stephen
    We present a learning-based style transfer algorithm for human portraits which significantly outperforms current state-of-the-art in computational overhead while still maintaining comparable visual quality. We show how to design a conditional generative adversarial network capable to reproduce the output of Fišer et al.'s patch-based method [FJS*17] that is slow to compute but can deliver state-of-the-art visual quality. Since the resulting end-to-end network can be evaluated quickly on current consumer GPUs, our solution enables first real-time high-quality style transfer to facial videos that runs at interactive frame rates. Moreover, in cases when the original algorithmic approach of Fišer et al. fails our network can provide a more visually pleasing result thanks to generalization. We demonstrate the practical utility of our approach on a variety of different styles and target subjects.
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    STALP: Style Transfer with Auxiliary Limited Pairing
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Futschik, David; Kucera, Michal; Lukác, Mike; Wang, Zhaowen; Shechtman, Eli; Sýkora, Daniel; Mitra, Niloy and Viola, Ivan
    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 translation network that can perform real-time semantically meaningful style transfer to a set of target images with similar content as the source image. A key added value of our approach is that it considers also consistency of target images during training. Although those have no stylized counterparts, we constrain the translation to keep the statistics of neural responses compatible with those extracted from the stylized source. In contrast to concurrent techniques that use a similar input, our approach better preserves important visual characteristics of the source style and can deliver temporally stable results without the need to explicitly handle temporal consistency. We demonstrate its practical utility on various applications including video stylization, style transfer to panoramas, faces, and 3D models.