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It struggles to create a likeness on the side profile but that's to be expected. Nose and mouth details lost and it ends up looking like someone else. Still it'

It struggles to create a likeness on the side profile but that's to be expected. Nose and mouth details lost and it ends up looking like someone else. Still it's very cool, but it can't perform miracles. A good test is when you have a front and side source image, such as police mug shot... Take this famous David Bowie mug shot. I also tried Jimi Hendrix and Jim Morrison, the side profile never looks like the person. https://ozimg.s3.amazonaws.com/temp/bowie_mug.jpg https://ozimg.s3.amazonaws.com/temp/bowie_mug.jpg An improvement to the engine might also accept a side profile source image to improve the details. Now that would be something!

Hydraulix989 9y ago
You're never going to get an accurate side profile from ONE photo (front); otherwise, you are pulling information out of thin air -- like "enhancing" an image by somehow zooming in 100x.
- exodust 9y ago
  Never say never. A smarter algorithm might analyse the light and shadow to a greater degree, and better predict side profile details. Or, take it further and have the algorithm silently check the internet for match of person and then look for more images of that person. Not cheating if the measure is "upload at least one image to start with, the service will then do the best it can to perform a miracle".
  - Hydraulix989 9y ago
    You're missing the point; if the information content isn't there (i.e. "no clues") and you're not providing it somehow (e.g. by "cheating" and providing more than one image from the Internet), there's literally no way to reconstruct it -- this reeks of the Nyquist sampling theorem. It's not something that can still be accomplished by someone who isn't a pessimist; it's provably impossible.
    - doublecorrect 9y ago
      That's, like, the entire opposite of the nyquist sampling theorem. Faces are a low dimensional space. Images are a higher dimensional space. https://en.m.wikipedia.org/wiki/Compressed_sensing https://en.m.wikipedia.org/wiki/Compressed_sensing
      - Hydraulix989 9y ago
        There just isn't a learn-able (or "un-learnable" for that matter) function _even_ on the restricted domain of frontal profile images that maps surjectively onto the MUCH larger space of possible 3D face reconstructions. Intuitively, maybe the side of my jaw is deformed in a way that is not visible from the front, for example -- how can any oracle recover this information without seeing the side of my face? Many people are mistakenly under the impression that certain signal reconstruction techniques like compressed sensing "violate" the Nyquist sampling theorem. Compressed sensing is still under the same umbrella of the Nyquist Sampling Theorem, as is CNN-based reconstruction (the technique used by this paper). I realize my analogy might have been poor; my claim is that there is still unrecoverable information loss.

Hydraulix989 9y ago

You're never going to get an accurate side profile from ONE photo (front); otherwise, you are pulling information out of thin air -- like "enhancing" an image by somehow zooming in 100x.

exodust 9y ago
Never say never. A smarter algorithm might analyse the light and shadow to a greater degree, and better predict side profile details. Or, take it further and have the algorithm silently check the internet for match of person and then look for more images of that person. Not cheating if the measure is "upload at least one image to start with, the service will then do the best it can to perform a miracle".
- Hydraulix989 9y ago
  You're missing the point; if the information content isn't there (i.e. "no clues") and you're not providing it somehow (e.g. by "cheating" and providing more than one image from the Internet), there's literally no way to reconstruct it -- this reeks of the Nyquist sampling theorem. It's not something that can still be accomplished by someone who isn't a pessimist; it's provably impossible.
  - doublecorrect 9y ago
    That's, like, the entire opposite of the nyquist sampling theorem. Faces are a low dimensional space. Images are a higher dimensional space. https://en.m.wikipedia.org/wiki/Compressed_sensing https://en.m.wikipedia.org/wiki/Compressed_sensing
    - Hydraulix989 9y ago
      There just isn't a learn-able (or "un-learnable" for that matter) function _even_ on the restricted domain of frontal profile images that maps surjectively onto the MUCH larger space of possible 3D face reconstructions. Intuitively, maybe the side of my jaw is deformed in a way that is not visible from the front, for example -- how can any oracle recover this information without seeing the side of my face? Many people are mistakenly under the impression that certain signal reconstruction techniques like compressed sensing "violate" the Nyquist sampling theorem. Compressed sensing is still under the same umbrella of the Nyquist Sampling Theorem, as is CNN-based reconstruction (the technique used by this paper). I realize my analogy might have been poor; my claim is that there is still unrecoverable information loss.