Capabilities, Limitations and Challenges of Style Transfer with CycleGANs: A Study on Automatic Ring Design Generation
Tomas Cabezon Pedroso, Javier Del Ser, Natalia Diaz-Rodr{\i}guez

TL;DR
This paper explores the use of CycleGANs for style transfer in 2D-3D design visualization, specifically for automatic ring design generation, aiming to enhance the product design process.
Contribution
It demonstrates the applicability of CycleGANs in transforming sketches into final renderings for jewelry design, establishing a foundation for integrating GANs into design workflows.
Findings
CycleGANs can effectively transfer style from sketches to rendered images.
The approach enables previewing final products before manufacturing.
This method reduces time and effort in the design visualization process.
Abstract
Rendering programs have changed the design process completely as they permit to see how the products will look before they are fabricated. However, the rendering process is complicated and takes a significant amount of time, not only in the rendering itself but in the setting of the scene as well. Materials, lights and cameras need to be set in order to get the best quality results. Nevertheless, the optimal output may not be obtained in the first render. This all makes the rendering process a tedious process. Since Goodfellow et al. introduced Generative Adversarial Networks (GANs) in 2014 [1], they have been used to generate computer-assigned synthetic data, from non-existing human faces to medical data analysis or image style transfer. GANs have been used to transfer image textures from one domain to another. However, paired data from both domains was needed. When Zhu et al.…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Computer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis
MethodsHuMan(Expedia)||How do I get a human at Expedia? · *Communicated@Fast*How Do I Communicate to Expedia? · Residual Connection · Batch Normalization · GAN Least Squares Loss · Tanh Activation · PatchGAN · Instance Normalization · Sigmoid Activation · Convolution
