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Aliaksandra Shysheya
Researcher at Samsung
Publications - 8
Citations - 842
Aliaksandra Shysheya is an academic researcher from Samsung. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 5, co-authored 5 publications receiving 412 citations.
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Proceedings ArticleDOI
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
TL;DR: This work presents a system that performs lengthy meta-learning on a large dataset of videos, and is able to frame few- and one-shot learning of neural talking head models of previously unseen people as adversarial training problems with high capacity generators and discriminators.
Proceedings ArticleDOI
Textured Neural Avatars
Aliaksandra Shysheya,Dmitry Ulyanov,Alexander Vakhitov,Victor Lempitsky,Egor Zakharov,Kara-Ali Aliev,Renat Bashirov,Egor Burkov,Karim Iskakov,Aleksei Ivakhnenko,Yury Malkov,Igor Pasechnik +11 more
TL;DR: In this article, a fully-convolutional network is used to directly map the configuration of body feature points w.r.t. the camera to the 2D texture coordinates of individual pixels in the image frame.
Book ChapterDOI
Fast Bi-Layer Neural Synthesis of One-Shot Realistic Head Avatars
TL;DR: A neural rendering-based system that creates head avatars from a single photograph by decomposing it into two layers that is compared to analogous state-of-the-art systems in terms of visual quality and speed.
Posted Content
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
TL;DR: In this article, a meta-learning approach is proposed to initialize the parameters of both the generator and discriminator in a person-specific way, so that training can be based on just a few images and done quickly.
Posted Content
Fast Bi-layer Neural Synthesis of One-Shot Realistic Head Avatars
TL;DR: In this paper, the authors propose a neural rendering-based system that creates head avatars from a single photograph by decomposing a person's appearance into two layers, a pose-dependent coarse image that is synthesized by a small neural network and a poseindependent texture image that contains highfrequency details.