Advanced 3D Artistic Image Generation with VAE-SDFCycleGAN
Generation of a 3-dimensional (3D)-based artistic image from a 2-dimensional (2D) image using a generative adversarial network (GAN) framework is challenging. Most existing artistic GAN-based frameworks lack robust algorithms lack suitable 3D data representations that can fit into GAN to prod...
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| Format: | Article |
| Language: | English |
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Informatics Department, Faculty of Computer Science Bina Darma University
2024-12-01
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| Series: | Journal of Information Systems and Informatics |
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| Online Access: | https://journal-isi.org/index.php/isi/article/view/900 |
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| _version_ | 1850023685596905472 |
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| author | Dorcas Oladayo Esan Pius Adewale Owolawi Chunling Tu |
| author_facet | Dorcas Oladayo Esan Pius Adewale Owolawi Chunling Tu |
| author_sort | Dorcas Oladayo Esan |
| collection | DOAJ |
| description | Generation of a 3-dimensional (3D)-based artistic image from a 2-dimensional (2D) image using a generative adversarial network (GAN) framework is challenging. Most existing artistic GAN-based frameworks lack robust algorithms lack suitable 3D data representations that can fit into GAN to produce high-quality 3D artistic images. To produce 3D artistic images from 2D image that considerably improves scalability and visual quality, this research integrates innovative variational autoencoder signed distance function, cycle generative adversarial network (VAE-SDFCycleGAN). The proposed method feeds a single 2D image into the network to produce a mesh-based 3D shape. The network encodes a 2D image of the 3D object into latent representations, and implicit surface representations of 3D images corresponding to those of 2D images are subsequently generated. VAE extracts feature from the two-dimensional input image and reconstructs a voxel-type grid using a signed distance function. Cycle GAN produces improved and high-quality 3D artistic images from 2D images. The publicly available COCO dataset was used to evaluate the proposed advanced 3D-VAE-SDFCycleGAN. The model produced a peak signal noise ratio (PSNR) of 31.35, mean square error (MSE) of 65.32, and structural similarity index measure (SSIM) of 0.772 which indicates the improved quality of the generated images. The results are compared with other traditional GAN methods and the results obtained show that the proposed method outperforms the others in terms of quantitative and qualitative evaluation metrics. |
| format | Article |
| id | doaj-art-0f28086a19ea4ef4b58f458f796a6416 |
| institution | DOAJ |
| issn | 2656-5935 2656-4882 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Informatics Department, Faculty of Computer Science Bina Darma University |
| record_format | Article |
| series | Journal of Information Systems and Informatics |
| spelling | doaj-art-0f28086a19ea4ef4b58f458f796a64162025-08-20T03:01:18ZengInformatics Department, Faculty of Computer Science Bina Darma UniversityJournal of Information Systems and Informatics2656-59352656-48822024-12-01642508252410.51519/journalisi.v6i4.900900Advanced 3D Artistic Image Generation with VAE-SDFCycleGANDorcas Oladayo Esan0Pius Adewale Owolawi1Chunling Tu2Tshwane University of Technology South AfricaTshwane University of Technology South AfricaTshwane University of Technology South AfricaGeneration of a 3-dimensional (3D)-based artistic image from a 2-dimensional (2D) image using a generative adversarial network (GAN) framework is challenging. Most existing artistic GAN-based frameworks lack robust algorithms lack suitable 3D data representations that can fit into GAN to produce high-quality 3D artistic images. To produce 3D artistic images from 2D image that considerably improves scalability and visual quality, this research integrates innovative variational autoencoder signed distance function, cycle generative adversarial network (VAE-SDFCycleGAN). The proposed method feeds a single 2D image into the network to produce a mesh-based 3D shape. The network encodes a 2D image of the 3D object into latent representations, and implicit surface representations of 3D images corresponding to those of 2D images are subsequently generated. VAE extracts feature from the two-dimensional input image and reconstructs a voxel-type grid using a signed distance function. Cycle GAN produces improved and high-quality 3D artistic images from 2D images. The publicly available COCO dataset was used to evaluate the proposed advanced 3D-VAE-SDFCycleGAN. The model produced a peak signal noise ratio (PSNR) of 31.35, mean square error (MSE) of 65.32, and structural similarity index measure (SSIM) of 0.772 which indicates the improved quality of the generated images. The results are compared with other traditional GAN methods and the results obtained show that the proposed method outperforms the others in terms of quantitative and qualitative evaluation metrics.https://journal-isi.org/index.php/isi/article/view/9003d image, variational autoencoder, cycle gan, artistic image, signed distance function. |
| spellingShingle | Dorcas Oladayo Esan Pius Adewale Owolawi Chunling Tu Advanced 3D Artistic Image Generation with VAE-SDFCycleGAN Journal of Information Systems and Informatics 3d image, variational autoencoder, cycle gan, artistic image, signed distance function. |
| title | Advanced 3D Artistic Image Generation with VAE-SDFCycleGAN |
| title_full | Advanced 3D Artistic Image Generation with VAE-SDFCycleGAN |
| title_fullStr | Advanced 3D Artistic Image Generation with VAE-SDFCycleGAN |
| title_full_unstemmed | Advanced 3D Artistic Image Generation with VAE-SDFCycleGAN |
| title_short | Advanced 3D Artistic Image Generation with VAE-SDFCycleGAN |
| title_sort | advanced 3d artistic image generation with vae sdfcyclegan |
| topic | 3d image, variational autoencoder, cycle gan, artistic image, signed distance function. |
| url | https://journal-isi.org/index.php/isi/article/view/900 |
| work_keys_str_mv | AT dorcasoladayoesan advanced3dartisticimagegenerationwithvaesdfcyclegan AT piusadewaleowolawi advanced3dartisticimagegenerationwithvaesdfcyclegan AT chunlingtu advanced3dartisticimagegenerationwithvaesdfcyclegan |