Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Drobyshev, Nikita"'
Autor:
Drobyshev, Nikita, Casademunt, Antoni Bigata, Vougioukas, Konstantinos, Landgraf, Zoe, Petridis, Stavros, Pantic, Maja
Head avatars animated by visual signals have gained popularity, particularly in cross-driving synthesis where the driver differs from the animated character, a challenging but highly practical approach. The recently presented MegaPortraits model has
Externí odkaz:
http://arxiv.org/abs/2404.19110
Autor:
Casademunt, Antoni Bigata, Mira, Rodrigo, Drobyshev, Nikita, Vougioukas, Konstantinos, Petridis, Stavros, Pantic, Maja
Speech-driven animation has gained significant traction in recent years, with current methods achieving near-photorealistic results. However, the field remains underexplored regarding non-verbal communication despite evidence demonstrating its import
Externí odkaz:
http://arxiv.org/abs/2305.08854
Autor:
Drobyshev, Nikita, Chelishev, Jenya, Khakhulin, Taras, Ivakhnenko, Aleksei, Lempitsky, Victor, Zakharov, Egor
In this work, we advance the neural head avatar technology to the megapixel resolution while focusing on the particularly challenging task of cross-driving synthesis, i.e., when the appearance of the driving image is substantially different from the
Externí odkaz:
http://arxiv.org/abs/2207.07621
Autor:
Safin, Aleksandr, Kan, Maxim, Drobyshev, Nikita, Voynov, Oleg, Artemov, Alexey, Filippov, Alexander, Zorin, Denis, Burnaev, Evgeny
Depth maps captured with commodity sensors are often of low quality and resolution; these maps need to be enhanced to be used in many applications. State-of-the-art data-driven methods of depth map super-resolution rely on registered pairs of low- an
Externí odkaz:
http://arxiv.org/abs/2105.12038
Autor:
Kan, Maxim, Aliev, Ruslan, Rudenko, Anna, Drobyshev, Nikita, Petrashen, Nikita, Kondrateva, Ekaterina, Sharaev, Maxim, Bernstein, Alexander, Burnaev, Evgeny
Publikováno v:
AIST2020
Deep learning shows high potential for many medical image analysis tasks. Neural networks can work with full-size data without extensive preprocessing and feature generation and, thus, information loss. Recent work has shown that the morphological di
Externí odkaz:
http://arxiv.org/abs/2006.15969
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Kan, Maxim, Aliev, Ruslan, Rudenko, Anna, Drobyshev, Nikita, Petrashen, Nikita, Kondrateva, Ekaterina, Sharaev, Maxim, Bernstein, Alexander, Burnaev, Evgeny
Publikováno v:
Recent Trends in Analysis of Images, Social Networks and Texts
Deep learning shows high potential for many medical image analysis tasks. Neural networks can work with full-size data without extensive preprocessing and feature generation and, thus, information loss. Recent work has shown that the morphological di