Toward Enhancing LightWeight GAN for Text-Guided Generation of Animated Character Faces
Traditional text-guided image generation methods primarily focus on modifying existing images or altering specific elements, which limits their applicability. This paper introduces a significant enhancement to the LightWeight-GAN model, originally designed for image generation from random noise, by...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11119659/ |
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| author | Sameh Zarif Abdalfatah Najjar Khalid Mohamed Amin Abdullah Alharbi Wail S. Elkilani Mahmoud A. Shawky Marian Wagdy |
| author_facet | Sameh Zarif Abdalfatah Najjar Khalid Mohamed Amin Abdullah Alharbi Wail S. Elkilani Mahmoud A. Shawky Marian Wagdy |
| author_sort | Sameh Zarif |
| collection | DOAJ |
| description | Traditional text-guided image generation methods primarily focus on modifying existing images or altering specific elements, which limits their applicability. This paper introduces a significant enhancement to the LightWeight-GAN model, originally designed for image generation from random noise, by transforming it into a text-guided generative system. The proposed approach enables the generation of high-quality animated character faces directly from textual descriptions. To achieve this, we incorporate a mapping network that refines textual inputs before feeding them into the generator, ensuring more precise feature representation. Additionally, we integrate contrastive language-image pretraining (CLIP) to verify the generated images and enforce stronger alignment between textual prompts and visual outputs. The model is trained on a specialized facial dataset, demonstrating its ability to generate semantically accurate and visually compelling character faces. Extensive experiments using the CartoonSet dataset validate the effectiveness of our approach, achieving an FID score of 29.8 across 10 000 generated images. These improvements significantly outperform existing text-to-image generation models, making our system a promising tool for applications in game development, animation, and virtual reality. |
| format | Article |
| id | doaj-art-fed2876745e4451d9fab51ed11ccde9b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-fed2876745e4451d9fab51ed11ccde9b2025-08-20T03:36:58ZengIEEEIEEE Access2169-35362025-01-011313997913999110.1109/ACCESS.2025.359592811119659Toward Enhancing LightWeight GAN for Text-Guided Generation of Animated Character FacesSameh Zarif0https://orcid.org/0000-0001-8644-4478Abdalfatah Najjar1Khalid Mohamed Amin2https://orcid.org/0000-0002-9594-8827Abdullah Alharbi3https://orcid.org/0000-0001-8617-1430Wail S. Elkilani4Mahmoud A. Shawky5https://orcid.org/0000-0003-3393-8460Marian Wagdy6https://orcid.org/0009-0007-5620-1337Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shebeen El-Kom, EgyptDepartment of Information Technology, Faculty of Computers and Information, Menoufia University, Shebeen El-Kom, EgyptDepartment of Information Technology, Faculty of Computers and Information, Menoufia University, Shebeen El-Kom, EgyptDepartment of Computer Science and Engineering, College of Applied Studies, King Saud University, Riyadh, Saudi ArabiaCollege of Applied Computer Science, King Saud University, Riyadh, Saudi ArabiaJames Watt School of Engineering, University of Glasgow, Glasgow, U.K.Department of Information Technology, Faculty of Computers and Information, Tanta University, Tanta, EgyptTraditional text-guided image generation methods primarily focus on modifying existing images or altering specific elements, which limits their applicability. This paper introduces a significant enhancement to the LightWeight-GAN model, originally designed for image generation from random noise, by transforming it into a text-guided generative system. The proposed approach enables the generation of high-quality animated character faces directly from textual descriptions. To achieve this, we incorporate a mapping network that refines textual inputs before feeding them into the generator, ensuring more precise feature representation. Additionally, we integrate contrastive language-image pretraining (CLIP) to verify the generated images and enforce stronger alignment between textual prompts and visual outputs. The model is trained on a specialized facial dataset, demonstrating its ability to generate semantically accurate and visually compelling character faces. Extensive experiments using the CartoonSet dataset validate the effectiveness of our approach, achieving an FID score of 29.8 across 10 000 generated images. These improvements significantly outperform existing text-to-image generation models, making our system a promising tool for applications in game development, animation, and virtual reality.https://ieeexplore.ieee.org/document/11119659/Animated faces generationLightWeight-GANtext-guided imagetext-to-image generation |
| spellingShingle | Sameh Zarif Abdalfatah Najjar Khalid Mohamed Amin Abdullah Alharbi Wail S. Elkilani Mahmoud A. Shawky Marian Wagdy Toward Enhancing LightWeight GAN for Text-Guided Generation of Animated Character Faces IEEE Access Animated faces generation LightWeight-GAN text-guided image text-to-image generation |
| title | Toward Enhancing LightWeight GAN for Text-Guided Generation of Animated Character Faces |
| title_full | Toward Enhancing LightWeight GAN for Text-Guided Generation of Animated Character Faces |
| title_fullStr | Toward Enhancing LightWeight GAN for Text-Guided Generation of Animated Character Faces |
| title_full_unstemmed | Toward Enhancing LightWeight GAN for Text-Guided Generation of Animated Character Faces |
| title_short | Toward Enhancing LightWeight GAN for Text-Guided Generation of Animated Character Faces |
| title_sort | toward enhancing lightweight gan for text guided generation of animated character faces |
| topic | Animated faces generation LightWeight-GAN text-guided image text-to-image generation |
| url | https://ieeexplore.ieee.org/document/11119659/ |
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