Dance energy style transfer using optical flow pattern and image-to-image translation networks
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| dc.contributor.author | Petryshyn, Sofiia
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| 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 |
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2022 [39]
