TY - GEN
T1 - Capabilities, Limitations and Challenges of Style Transfer with CycleGANs
T2 - 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2022, held in conjunction with the 17th International Conference on Availability, Reliability and Security, ARES 2022
AU - Cabezon Pedroso, Tomas
AU - Ser, Javier Del
AU - Díaz-Rodríguez, Natalia
N1 - Publisher Copyright:
© 2022, IFIP International Federation for Information Processing.
PY - 2022
Y1 - 2022
N2 - 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. 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 final 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 final 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.
AB - 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. 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 final 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 final 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.
KW - Automatic design
KW - CycleGAN
KW - Deep learning
KW - Generative adversarial networks
KW - Image-to-Image translation
KW - Jewelry design
UR - http://www.scopus.com/inward/record.url?scp=85136985869&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-14463-9_11
DO - 10.1007/978-3-031-14463-9_11
M3 - Conference contribution
AN - SCOPUS:85136985869
SN - 9783031144622
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 168
EP - 187
BT - Machine Learning and Knowledge Extraction - 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2022, Proceedings
A2 - Holzinger, Andreas
A2 - Holzinger, Andreas
A2 - Holzinger, Andreas
A2 - Kieseberg, Peter
A2 - Tjoa, A Min
A2 - Weippl, Edgar
A2 - Weippl, Edgar
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 23 August 2022 through 26 August 2022
ER -