Grigory Antipov, et al. in their 2017 paper titled “Face Aging With Conditional Generative Adversarial Networks” use GANs to generate photographs of faces with different apparent ages, from younger to older.
Example of Photographs of Faces Generated With a GAN With Different Apparent Ages.Taken from Face Aging With Conditional Generative Adversarial Networks, 2017.
Zhifei Zhang, in their 2017 paper titled “Age Progression/Regression by Conditional Adversarial Autoencoder” use a GAN based method for de-aging photographs of faces.
Example of Using a GAN to Age Photographs of FacesTaken from Age Progression/Regression by Conditional Adversarial Autoencoder, 2017.
Huikai Wu, et al. in their 2017 paper titled “GP-GAN: Towards Realistic High-Resolution Image Blending” demonstrate the use of GANs in blending photographs, specifically elements from different photographs such as fields, mountains, and other large structures.
Example of GAN-based Photograph Blending.Taken from GP-GAN: Towards Realistic High-Resolution Image Blending, 2017.
Christian Ledig, et al. in their 2016 paper titled “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network” demonstrate the use of GANs, specifically their SRGAN model, to generate output images with higher, sometimes much higher, pixel resolution.
Example of GAN-Generated Images With Super Resolution. Taken from Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, 2016.
Huang Bin, et al. in their 2017 paper tilted “High-Quality Face Image SR Using Conditional Generative Adversarial Networks” use GANs for creating versions of photographs of human faces.
Example of High-Resolution Generated Human FacesTaken from High-Quality Face Image SR Using Conditional Generative Adversarial Networks, 2017.
Subeesh Vasu, et al. in their 2018 paper tilted “Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network” provide an example of GANs for creating high-resolution photographs, focusing on street scenes.
Example of High-Resolution GAN-Generated Photographs of Buildings.Taken from Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network, 2018.
Deepak Pathak, et al. in their 2016 paper titled “Context Encoders: Feature Learning by Inpainting” describe the use of GANs, specifically Context Encoders, to perform photograph inpainting or hole filling, that is filling in an area of a photograph that was removed for some reason.
Example of GAN-Generated Photograph Inpainting Using Context Encoders.Taken from Context Encoders: Feature Learning by Inpainting describe the use of GANs, specifically Context Encoders, 2016.
Raymond A. Yeh, et al. in their 2016 paper titled “Semantic Image Inpainting with Deep Generative Models” use GANs to fill in and repair intentionally damaged photographs of human faces.
Example of GAN-based Inpainting of Photographs of Human FacesTaken from Semantic Image Inpainting with Deep Generative Models, 2016.
Yijun Li, et al. in their 2017 paper titled “Generative Face Completion” also use GANs for inpainting and reconstructing damaged photographs of human faces.
Example of GAN Reconstructed Photographs of FacesTaken from Generative Face Completion, 2017.
Donggeun Yoo, et al. in their 2016 paper titled “Pixel-Level Domain Transfer” demonstrate the use of GANs to generate photographs of clothing as may be seen in a catalog or online store, based on photographs of models wearing the clothing.
Example of Input Photographs and GAN-Generated Clothing PhotographsTaken from Pixel-Level Domain Transfer, 2016.
Carl Vondrick, et al. in their 2016 paper titled “Generating Videos with Scene Dynamics” describe the use of GANs for video prediction, specifically predicting up to a second of video frames with success, mainly for static elements of the scene.
Example of Video Frames Generated With a GAN.Taken from Generating Videos with Scene Dynamics, 2016.
Jiajun Wu, et al. in their 2016 paper titled “Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling” demonstrate a GAN for generating new three-dimensional objects (e.g. 3D models) such as chairs, cars, sofas, and tables.
Example of GAN-Generated Three Dimensional Objects.Taken from Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Matheus Gadelha, et al. in their 2016 paper titled “3D Shape Induction from 2D Views of Multiple Objects” use GANs to generate three-dimensional models given two-dimensional pictures of objects from multiple perspectives.
Example of Three-Dimensional Reconstructions of a Chair From Two-Dimensional Images.Taken from 3D Shape Induction from 2D Views of Multiple Objects, 2016.
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