In a paper published on Facebook’s research site, and spotted by Motherboard, Brian Dolhansky and Cristian Canton Ferrer show how an “Exemplar Generative Adversarial Network” (ExGAN) can be trained to accurately retouch images, all while avoiding the “uncanny valley” effect.
“In order to further verify our method, we performed a perceptual A/B test to judge the quality of the obtained results,” the paper says. “The test presented two pairs of images of the same person: one pair contained a reference image and a real image, while the other pair contained the same reference image and a different, in-painted image.
“The photographs were selected from our internal dataset, which offered more variety in pose and lighting than generic celebrity datasets. The participants were asked to pick the pair of images that were not in-painted. 54% of the time, participants either picked the generated image or were unsure which was the real image pair.
“The most common cause of failure was due to occlusions such as glasses or hair covering the eyes in the original or reference images. We suspect that with further training with more variable sized masks (that may overlap hair or glasses) could alleviate this issue.”
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Original source: http://www.trustedreviews.com/news/facebook-fix-blink-photo-3489379