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- W4362502178 abstract "This paper explores a method for creating 3D models of video artwork based on a fluid phenomenon called the Sound of Ikebana artwork. A process of using multiple generative adversarial networks (GANs) to reconstruct and predict the shape of the fluid artworks from two-dimensional reference photos was proposed. This is an extension of our previous efforts with Wasserstein GAN enhancements to predict the shape of the unmapped part and correct the texture. The experiment’s results show that our process can reconstruct 3D arts without having large amount of 3D training data." @default.
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- W4362502178 date "2023-01-01" @default.
- W4362502178 modified "2023-10-05" @default.
- W4362502178 title "A Deep Learning-Based Approach for Generating 3D Models of Fluid Arts" @default.
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- W4362502178 doi "https://doi.org/10.1007/978-3-031-28993-4_4" @default.
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