Han Zhang, et al. in their 2016 paper titled “StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks” demonstrate the use of GANs, specifically their StackGAN to generate realistic looking photographs from textual descriptions of simple objects like birds and flowers.
Example of Textual Descriptions and GAN-Generated Photographs of BirdsTaken from StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks, 2016.
Scott Reed, et al. in their 2016 paper titled “Generative Adversarial Text to Image Synthesis” also provide an early example of text to image generation of small objects and scenes including birds, flowers, and more.
Example of Textual Descriptions and GAN-Generated Photographs of Birds and Flowers.Taken from Generative Adversarial Text to Image Synthesis.
Ayushman Dash, et al. provide more examples on seemingly the same dataset in their 2017 paper titled “TAC-GAN – Text Conditioned Auxiliary Classifier Generative Adversarial Network“.
Scott Reed, et al. in their 2016 paper titled “Learning What and Where to Draw” expand upon this capability and use GANs to both generate images from text and use bounding boxes and key points as hints as to where to draw a described object, like a bird.
Example of Photos of Object Generated From Text and Position Hints With a GAN.Taken from Learning What and Where to Draw, 2016.
Ting-Chun Wang, et al. in their 2017 paper titled “High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs” demonstrate the use of conditional GANs to generate photorealistic images given a semantic image or sketch as input.
Example of Semantic Image and GAN-Generated Cityscape Photograph.Taken from High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs, 2017.
Specific examples included:
They also demonstrate an interactive editor for manipulating the generated image.
Rui Huang, et al. in their 2017 paper titled “Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis” demonstrate the use of GANs for generating frontal-view (i.e. face on) photographs of human faces given photographs taken at an angle. The idea is that the generated front-on photos can then be used as input to a face verification or face identification system.
Example of GAN-based Face Frontal View Photo GenerationTaken from Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis, 2017.
Liqian Ma, et al. in their 2017 paper titled “Pose Guided Person Image Generation” provide an example of generating new photographs of human models with new poses.
Example of GAN-Generated Photographs of Human PosesTaken from Pose Guided Person Image Generation, 2017.
Yaniv Taigman, et al. in their 2016 paper titled “Unsupervised Cross-Domain Image Generation” used a GAN to translate images from one domain to another, including from street numbers to MNIST handwritten digits, and from photographs of celebrities to what they call emojis or small cartoon faces.
Example of Celebrity Photographs and GAN-Generated Emojis.Taken from Unsupervised Cross-Domain Image Generation, 2016.
Guim Perarnau, et al. in their 2016 paper titled “Invertible Conditional GANs For Image Editing” use a GAN, specifically their IcGAN, to reconstruct photographs of faces with specific specified features, such as changes in hair color, style, facial expression, and even gender.
Example of Face Photo Editing with IcGAN.Taken from Invertible Conditional GANs For Image Editing, 2016.
Ming-Yu Liu, et al. in their 2016 paper titled “Coupled Generative Adversarial Networks” also explore the generation of faces with specific properties such as hair color, facial expression, and glasses. They also explore the generation of other images, such as scenes with varied color and depth.
Example of GANs used to Generate Faces With and Without Blond Hair.Taken from Coupled Generative Adversarial Networks, 2016.
Andrew Brock, et al. in their 2016 paper titled “Neural Photo Editing with Introspective Adversarial Networks” present a face photo editor using a hybrid of variational autoencoders and GANs. The editor allows rapid realistic modification of human faces including changing hair color, hairstyles, facial expression, poses, and adding facial hair.
Example of Face Editing Using the Neural Photo Editor Based on VAEs and GANs.Taken from Neural Photo Editing with Introspective Adversarial Networks, 2016.
He Zhang, et al. in their 2017 paper titled “Image De-raining Using a Conditional Generative Adversarial Network” use GANs for image editing, including examples such as removing rain and snow from photographs.
Example of Using a GAN to Remove Rain From PhotographsTaken from Image De-raining Using a Conditional Generative Adversarial Network
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