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of 8
pro vyhledávání: '"Futschik, David"'
We introduce StyleReiser, an example-based video stylization method that transfers style from a given keyframe to the entire video sequence while maintaining visual consistency even in distant frames where the scene structure may change significantly
Externí odkaz:
http://arxiv.org/abs/2409.15341
Autor:
Dai, Peng, Tan, Feitong, Xu, Qiangeng, Futschik, David, Du, Ruofei, Fanello, Sean, Qi, Xiaojuan, Zhang, Yinda
Video generation models have demonstrated great capabilities of producing impressive monocular videos, however, the generation of 3D stereoscopic video remains under-explored. We propose a pose-free and training-free approach for generating 3D stereo
Externí odkaz:
http://arxiv.org/abs/2407.00367
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:
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:
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
Autor:
Texler, Ondřej, Futschik, David, Fišer, Jakub, Lukáč, Michal, Lu, Jingwan, Shechtman, Eli, Sýkora, Daniel
Publikováno v:
In Computers & Graphics April 2020 87:62-71
Autor:
Futschik, David, Chai, Menglei, Cao, Chen, Ma, Chongyang, Stoliar, Aleksei, Korolev, Sergey, Tulyakov, Sergey, Kučera, Michal, Sýkora, Daniel
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 generat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2636f13631f00164f8cc77d348c1006e