Zobrazeno 1 - 10
of 137
pro vyhledávání: '"Sykora, Daniel"'
We introduce StructuReiser, a novel video-to-video translation method that transforms input videos into stylized sequences using a set of user-provided keyframes. Unlike existing approaches, StructuReiser maintains strict adherence to the structural
Externí odkaz:
http://arxiv.org/abs/2409.15341
Autor:
Futschik, David, Ritland, Kelvin, Vecore, James, Fanello, Sean, Orts-Escolano, Sergio, Curless, Brian, Sýkora, Daniel, Pandey, Rohit
We introduce light diffusion, a novel method to improve lighting in portraits, softening harsh shadows and specular highlights while preserving overall scene illumination. Inspired by professional photographers' diffusers and scrims, our method softe
Externí odkaz:
http://arxiv.org/abs/2305.04745
Autor:
Kolkin, Nicholas, Kucera, Michal, Paris, Sylvain, Sykora, Daniel, Shechtman, Eli, Shakhnarovich, Greg
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 approach is based on explicitly replacing neural features extracted from th
Externí odkaz:
http://arxiv.org/abs/2203.13215
Autor:
Sykora, Daniel, Rosenbaum, Andrew N., Churchill, Robert A., Kim, B. Michelle, Elwazir, Mohamed Y., Bois, John P., Giudicessi, John R., Bratcher, Melanie, Young, Kathleen A., Ryan, Sami M., Sugrue, Alan M., Killu, Ammar M., Chareonthaitawee, Panithaya, Kapa, Suraj, Deshmukh, Abhishek J., Abou Ezzeddine, Omar F., Cooper, Leslie T., Siontis, Konstantinos C.
Publikováno v:
In Heart Rhythm October 2024 21(10):1978-1986
Publikováno v:
In Mayo Clinic Proceedings September 2024 99(9):1482-1487
Autor:
Futschik, David, Kučera, Michal, Lukáč, Michal, Wang, Zhaowen, Shechtman, Eli, Sýkora, Daniel
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 tr
Externí odkaz:
http://arxiv.org/abs/2110.10501
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 editing tasks as an operation that affects pixel values in the entire image
Externí odkaz:
http://arxiv.org/abs/2110.06269
Autor:
Sykora, Daniel, Churchill, Robert A., Hodge, David O., Callori, Steven, Houghton, Damon E., McBane, Robert D., Wysokinski, Waldemar E.
Publikováno v:
In Thrombosis Research July 2024 239
Autor:
Kim, B. Michelle, Sykora, Daniel, Rosenbaum, Andrew N., Ahmed, Enas, Churchill, Robert A., Bratcher, Melanie, Elwazir, Mohamed Y., Bois, John P., Giudicessi, John R., Sugrue, Alan M., Killu, Ammar M., Kapa, Suraj, Deshmukh, Abhishek J., Asirvatham, Samuel J., Cooper, Leslie T., Abou Ezzeddine, Omar F., Siontis, Konstantinos C.
Publikováno v:
In Heart Rhythm
Autor:
Texler, Ondřej, Futschik, David, Kučera, Michal, Jamriška, Ondřej, Sochorová, Šárka, Chai, Menglei, Tulyakov, Sergey, Sýkora, Daniel
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 the sequence. Its key advantage is that the resulting stylization is s
Externí odkaz:
http://arxiv.org/abs/2004.14489