Example #1
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This image shows the face replacement algorithm being applied to
an arbitrary portrait of an individual's face. The image at the
left is the original, untouched image. The middle image represents
a front-facing, fully-lit target image that we wish to place
over top of the original face. The image to the right is the final
result of the face replacement. Notice that the lighting has been
recreated fairly well, and the skin tone around the new photograph
is fairly consistent. The algorithm failed to convert some of the
extremely bright areas of the original face (such as the forehead
and neck) during the replacement process, largely due to the use
of a simple flesh detection algorithm. One other problem is the
new "manly" facial features that the replacement face has
taken on. This is because the original face had a relatively long
and sharp chin, whereas the replacement woman has a soft and rounded
chin. The algorithm currently does not take this into consideration.
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Example #2
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This image shows the face replacement algorithm being applied to
the same original photograph in the previous example, but with a
new replacement face. Notice that the final output image exhibits
the same problems as the previous result, namely the bright spots
in the top right area of the forehead and the bottom right area
of the neck. Nevertheless, the remaining parts of the conversion
are convincing.
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Example #3
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This image shows how the algorithm handles head poses that are
not necessarily front-facing. The image at the left is the original
picture, and the image in the middle is the new image after replacing
Steven Seagal's face with the person on the right. While the result
is fairly good, there are a few noticeable problems. The first is
a lack of expression on the new face, which is clearly evident in
the original. Another problem is the lack of shadows in the jaw
area of the replaced face. Additionally, the replaced face exhibits
somewhat of a "plastic" look, largely due to our use of a
use of a Lambertian reflectance model.
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Example #4
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This example shows what happens if our flesh detection algorithm
fails. The photograph on the left is the original image. The middle image
shows the detected flesh pixels in white. Notice how some of the
background pixels have been classified incorrectly as flesh pixels.
Since these pixels are connected to the facial flesh area, our flood
fill algorithm also considers the connected background pixels as
flesh as well. As a result, the face replacement image to the right
also has some background and hair pixels being incorrectly converted
to the new replacement face flesh tones.
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Example #5
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This example image shows the result of augmenting some virtual
object into the original scene to see how it becomes illuminated.
In this case, we augment a virtual checkered hat onto Steven
Seagal's head. Notice how the light estimation is fairly accurate,
causing the hat to be realistically illuminated from the left of the
scene.
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