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Information × Registration Number 2122U006305, Article popup.category Препринт Title Dance energy style transfer using optical flow pattern and image-to-image translation networks (AI translated) popup.author Petryshyn SofiiaPetryshyn Sofiia popup.publication 01-01-2022 popup.source_user Український католицький університет popup.source https://hdl.handle.net/20.500.14570/4405 popup.publisher Description 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. popup.nrat_date 2025-11-05 Close