Title : Morphing Celebrities
link : Morphing Celebrities
Morphing Celebrities
In this video, a computer invents faces that look like celebrities. They're 100% artificial, created by extracting information from a huge database of real celebrity images. (Link to video)
The faces slowly morph from one individual to another, shifting from male to female, melting gradually from one ethnicity to another. The only thing that stays constant is the position of the eyes.
I get the feeling that I'm in the presence of an alien artistic mind with a bizarre liquid creativity. This non-human mind doesn't observe the boundaries between groups of people and sees every face as part of a shifting continuum.
Some faces almost look plausible, even though they're synthetic.
But there are in-between stages that look monstrous, bizarre, and sometimes beautiful. Textures become liquid or crusty like lava. The transitional images appear to be creative interpolations, rather than linear in-betweens.
The images are produced by an artificial-intelligence algorithm called a generative adversarial network (GAN).
One algorithm is optimized to generate images while another is fine-tuned to distinguish a plausibly real face from a fake one. The computer is trained using this "creator vs. critic" dynamic, starting with low resolution images, and adding detail and resolution in stages.
This kind of algorithm can also be applied to objects. If you start this video at 4:00, it shows computer-invented forms that morph into each other, but still stay within certain categories. (YouTube)
For example, the synthetic images above are generated within the categories of horse, sofa, bus, church/outdoor, and bicycle.
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Read the scientific paper
For example, the synthetic images above are generated within the categories of horse, sofa, bus, church/outdoor, and bicycle.
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Read the scientific paper
Publication: Progressive Growing of GANs for Improved Quality, Stability, and Variation
Authors: Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen (NVIDIA and Aalto University)
Previously on GJ
Text-to-image synthesis
Authors: Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen (NVIDIA and Aalto University)
Previously on GJ
Text-to-image synthesis
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