A comprehensive survey on generative adversarial networks used for synthesizing multimedia content

Kumar, Lalit; Singh, Dushyant Kumar

doi:10.1007/s11042-023-15138-x

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…These networks undergo adversarial training, where the generator aims to create images capable of deceiving the discriminator, while the discriminator strives to accurately discern the generated images. GANs have proven effective in generating top‐notch facial images, with notable advancements seen in recent GAN variations like StyleGAN and BigGAN, showcasing impressive outcomes in this domain 3,5 …”

Section: Related Workmentioning

confidence: 99%

“…In this application, a GAN consists of a generator network trained to create realistic facial images and a discriminator network trained to distinguish between authentic faces and those generated by the generator. Through an adversarial training process, the generator refines its ability to produce increasingly convincing facial images, while the discriminator simultaneously improves its capability to discern real from generated faces 2,3 . This dynamic interplay results in the generation of high‐quality and natural‐looking face images, making GANs a powerful tool for tasks such as face synthesis, augmentation, and facial expression manipulation.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, GANs cannot guarantee the confidentiality and security of the generated images, as the generator may generate images of real people. Therefore, it is important to consider the ethical implications of using GANs to generate images of people and obtain consent from images used in the training dataset 3 . This manuscript is totally based on realistic face image generation for automated content creation of Video Logs.…”

Section: Introductionmentioning

confidence: 99%

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Diversified realistic face image generation GAN for human subjects in multimedia content creation

Kumar,

Singh

2024

Computer Animation & Virtual

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Face image generation plays an important role in generating innovative and unique multimedia content using the GAN model. With these qualities of the GAN model, they have numerous challenges in the human face image generation. The problems encountered in the generation of facial images are like blurriness in images, incomplete details in the generated facial images, high computational power requirements, and so forth. In this manuscript, we proposed a GAN model that utilizes the composite strength of VGG‐16 and ResNet‐50's models to overcome those difficulties. It uses VGG‐16 to build a discriminator model to discriminate between real and fake images. The generator model utilizes a combination of components from the ResNet‐50 and VGG‐16 models to enhance the image generation process at each iteration, resulting in the creation of realistic face images. The proposed DRFI GAN (Diversified and Realistic Face Image Generation GAN) model's generator achieves an impressive low FID score of 20.50, which is less than existing state‐of‐the‐art approaches. Furthermore, our findings indicate that the images generated by the DRFI GAN model exhibit 10%–15% greater efficiency and realism with reduced training time compared to existing state‐of‐the‐art methods with lower FID scores.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Diversified realistic face image generation GAN for human subjects in multimedia content creation

Kumar,

Singh

2024

Computer Animation & Virtual

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“…Teknik yang digunakan antara lain generative adversarial networks, variational autoencoders, transformer-based model, dan deep generative models lainnya untuk mensintesis dan memanipulasi gambar fashion dari teks [78].…”

Section: Oxford 102 Flowerunclassified

Sintesis Teks Ke Gambar: Tinjauan Atas Dataset

Arifin

2024

EEICT

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Penelitian ini bertujuan untuk melakukan analisis mendalam terhadap berbagai dataset yang digunakan dalam riset sintesis teks ke gambar. Fokus utama penelitian ini adalah pada pemahaman karakteristik masing-masing dataset, pengaruh pemilihan dataset terhadap hasil penelitian, serta keunggulan dan kelemahan setiap dataset. Beberapa dataset yang diteliti meliputi MS COCO, CUB-200-2011, dan Oxford 102 Flower, bersama dengan dataset-domain khusus lainnya yang relevan. Metode penelitian mencakup analisis deskriptif terhadap jumlah gambar, karakteristik visual, dan deskripsi teks yang melibatkan setiap dataset. Data yang diperoleh dianalisis secara kualitatif untuk mendapatkan wawasan mendalam tentang setiap dataset. Hasil analisis diharapkan dapat memberikan panduan bagi peneliti dalam memilih dataset yang sesuai dengan tujuan penelitian mereka dalam sintesis teks ke gambar. Penelitian ini diakhiri dengan rekomendasi dan kesimpulan yang merangkum temuan utama dan relevansinya dalam konteks riset ini.

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Pose image generation for video content creation using controlled human pose image generation GAN

Kumar,

Singh

2023

Multimed Tools Appl

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A comprehensive survey on generative adversarial networks used for synthesizing multimedia content

Cited by 7 publications

References 42 publications

Diversified realistic face image generation GAN for human subjects in multimedia content creation

Diversified realistic face image generation GAN for human subjects in multimedia content creation

Sintesis Teks Ke Gambar: Tinjauan Atas Dataset

Pose image generation for video content creation using controlled human pose image generation GAN

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