Dance energy style transfer using optical flow pattern and image-to-image  translation networks

Petryshyn, Sofiia

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Students Research & Project Works | Роботи студентів
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Факультет прикладних наук
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Бакалаврська програма "Комп'ютерні науки"
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2022
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dc.contributor.author	Petryshyn, Sofiia
dc.date.accessioned	2024-02-14T09:04:28Z
dc.date.available	2024-02-14T09:04:28Z
dc.date.issued	2022
dc.identifier.citation	Petryshyn, Sofiia. Dance energy style transfer using optical flow pattern and image-to-image translation networks / Petryshyn, Sofiia; Supervisor: Roman Vey, Lyubomyr Senyk; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2022. – 47 p.	uk
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/4405
dc.language.iso	en	uk
dc.title	Dance energy style transfer using optical flow pattern and image-to-image translation networks	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	Generative models was a topic of interest in a last year’s research. ‘Can Machines be More Creative than Humans?’ - the answer to this question is generative art. General thought that ’Generative art incorporates a self-governed or autonomous system’ is no longer relevant since deep learning techniques have made rapid progress in conditional image generation. This work addresses image-to-image translation problem. Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In this work, we aim to model a distribution of possible outputs in a conditional generative modeling setting. We train conditional GAN with additional changes, such as one more channel as an input, different pairs in datasets, and changes in input images sizes. As a result, we propose a framework that efficiently generates energy-flow video visuals from a single input-video where a person is dancing.	uk