@misc{10481/78078,
year = {2022},
month = {7},
url = {https://hdl.handle.net/10481/78078},
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
signi cant 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  rst 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. introduced the
CycleGAN model, the elimination of this expensive constraint permitted
transforming one image from one domain into another, without the
need for paired data. This work validates the applicability of CycleGANs
on style transfer from an initial sketch to a  nal render in 2D that represents
a 3D design, a step that is paramount in every product design
process. We inquiry the possibilities of including CycleGANs as part of
the design pipeline, more precisely, applied to the rendering of ring designs.
Our contribution entails a crucial part of the process as it allows
the customer to see the  nal product before buying. This work sets a basis
for future research, showing the possibilities of GANs in design and
establishing a starting point for novel applications to approach crafts
design.},
organization = {MCIN/AEI	IJC2019-039152-I},
organization = {ESF Investing in your future	IJC2019-039152-I},
organization = {Google Research Scholar Program},
organization = {Basque Government ELKARTEK program (3KIA project)	KK-2020/00049
research group MATHMODE	T1294-19},
publisher = {Springer},
keywords = {Deep learning},
keywords = {Generative Adversarial Networks},
keywords = {Automatic Design},
keywords = {Image-to-image translation},
keywords = {Jewelry design},
keywords = {CycleGAN},
title = {Capabilities, Limitations and Challenges of Style Transfer with CycleGANs: A Study on Automatic Ring Design Generation},
author = {Cabezón Pedroso, Tomás and Díaz Rodríguez, Natalia Ana},
}