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ADVANCED STUDIES IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Computer vision approaches in high-fidelity multimedia content synthesis
A. V. Kuznetsov, D. V. Dimitrov, A. Yu. Groshev, P. P. Paramonov, A. A. Mal’tseva Sber AI, Moscow
Abstract:
The development of deep learning technologies inevitably generates new tasks and their solutions in areas, such as computer vision, VR/AR technologies, video analytics, multimodal learning, etc. With increasing availability of high-performance computers, many modern methods and tools for digital data processing become widely applicable, including in personal application research. This tendency can easily be traced in the increasing number of open-source solutions that can easily be executed at well-known resources, such as Google Colab. In this paper, we describe results regarding the development and study of breakthrough technologies of high-quality multimedia synthesis, which have wide applications in tasks, such as face swapping.
Keywords:
face swap, GHOST, one shot, image synthesis, video synthesis.
Citation:
A. V. Kuznetsov, D. V. Dimitrov, A. Yu. Groshev, P. P. Paramonov, A. A. Mal’tseva, “Computer vision approaches in high-fidelity multimedia content synthesis”, Dokl. RAN. Math. Inf. Proc. Upr., 508 (2022), 109–110; Dokl. Math., 106:suppl. 1 (2022), S99–S100
Linking options:
https://www.mathnet.ru/eng/danma347 https://www.mathnet.ru/eng/danma/v508/p109
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Abstract page: | 48 | References: | 16 |
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