Could this be the technological innovation that hairstylists have been dying for? I'm sure a majority of us have had a bad haircut or two. But hopefully, with this AI, you'll never have to guess what a new haircut will look like ever again.
This AI can transfer a new hairstyle and/or color to a portrait to see how it would look like before committing to the change. Learn more about it below!
►The full article: https://www.louisbouchard.ai/barbershop/
►Peihao Zhu et al., (2021), Barbershop, https://arxiv.org/pdf/2106.01505.pdf
►Project link: https://zpdesu.github.io/Barbershop/
►Code: https://github.com/ZPdesu/Barbershop
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This article is not about a new technology in itself.
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Instead, it is about a new and exciting application of GANs.
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Indeed, you saw the title, and it wasn't clickbait.
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This AI can transfer your hair to see how it would look like before committing to the
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change.
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We all know that it may be hard to change your hairstyle even if you'd like to.
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Well, at least for myself, I'm used to the same haircut for years, telling my hairdresser
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"same as last time" every 3 or 4 months even if I'd like a change.
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I just can't commit, afraid it would look weird and unusual.
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Of course, this all in our head as we are the only ones caring about our haircut, but
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this tool could be a real game-changer for some of us, helping us to decide whether or
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not to commit to such a change having great insights on how it will look on us.
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Nonetheless, these moments where you can see in the future before taking a guess are rare.
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Even if it's not totally accurate, it's still pretty cool to have such an excellent approximation
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of how something like a new haircut could look like, relieving us of some of the stress
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of trying something new while keeping the exciting part.
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Of course, haircuts are very superficial compared to more useful applications.
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Still, it is a step forward towards "seeing in the future" using AI, which is pretty cool.
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Indeed, this new technique sort of enables us to predict the future, even if it's just
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the future of our haircut.
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But before diving into how it works, I am curious to know what you think about this.
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In any other field: What other application(s) would you like to see using AI to "see into
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the future"?
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It can change not only the style of your hair but also the color from multiple image examples.
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You can basically give three things to the algorithm:
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a picture of yourself a picture of someone with the hairstyle you
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would like to have and another picture (or the same one) of the hair
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color you would like to tryand it merges everything on yourself realistically.
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The results are seriously impressive.
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If you do not trust my judgment, as I would completely understand based on my artistic
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skill level, they also conducted a user study on 396 participants.
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Their solution was preferred 95 percent of the time!
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Of course, you can find more details about this study in the references below if this
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seems too hard to believe.
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As you may suspect, we are playing with faces here, so it is using a very similar process
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as the past papers I covered, changing the face into cartoons or other styles that are
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all using GANs.
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Since it is extremely similar, I'll let you watch my other videos where I explained how
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GANs work in-depth, and I'll focus on what is new with this method here and why it works
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so well.
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A GAN architecture can learn to transpose specific features or styles of an image onto
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another.
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The problem is that they often look unrealistic because of the lighting differences, occlusions
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it may have, or even simply the position of the head that are different in both pictures.
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All of these small details make this problem very challenging, causing artifacts in the
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generated image.
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Here's a simple example to better visualize this problem, if you take the hair of someone
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from a picture taken in a dark room and try to put it on yourself outside in daylight,
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even if it is transposed perfectly on your head, it will still look weird.
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Typically, these other techniques using GANs try to encode the pictures' information and
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explicitly identify the region associated with the hair attributes in this encoding
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to switch them.
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It works well when the two pictures are taken in similar conditions, but it won't look real
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most of the time for the reasons I just mentioned.
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Then, they had to use another network to fix the relighting, holes, and other weird artifacts
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caused by the merging.
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So the goal here was to transpose the hairstyle and color of a specific picture onto your
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own picture while changing the results to follow the lighting and property of your picture
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to make it convincing and realistic all at once, reducing the steps and sources of errors.
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If this last paragraph was unclear, I strongly recommend watching the video at the end of
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this article as there are more visual examples to help to understand.
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To achieve that, Peihao Zhu et al. added a missing but essential alignment step to GANs.
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Indeed, instead of simply encoding the images and merge them, it slightly alters the encoding
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following a different segmentation mask to make the latent code from the two images more
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similar.
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As I mentioned, they can both edit the structure and the style or appearance of the hair.
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Here, the structure is, of course, the geometry of the hair, telling us if it's curly, wavy,
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or straight.
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If you've seen my other videos, you already know that GANs encode the information using
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convolutions.
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This means it uses kernels to downscale the information at each layer and makes it smaller
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and smaller, thus iteratively removing spatial details while giving more and more value to
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general information to the resulting output.
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This structural information is obtained, as always, from the early layers of the GAN,
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so before the encoding becomes too general and, well, too encoded to represent spatial
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features.
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Appearance refers to the deeply encoded information, including hair color, texture, and lighting.
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You know where the information is taken from the different images, but now, how do they
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merge this information and make it look more realistic than previous approaches?
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This is done using segmentation maps from the images.
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And more precisely, generating this wanted new image based on an aligned version of our
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target and reference image.
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The reference image is our own image, and the target image the hairstyle we want to
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apply.
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These segmentation maps tell us what the image contains and where it is, hair, skin, eyes,
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nose, etc.
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Using this information from the different images, they can align the heads following
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the target image structure before sending the images to the network for encoding using
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a modified StyleGAN2-based architecture.
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One that I already covered numerous times.
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This alignment makes the encoded information much more easily comparable and reconstructable.
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Then, for the appearance and illumination problem, they find an appropriate mixture
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ratio of these appearances encodings from the target and reference images for the same
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segmented regions making it look as real as possible.
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Here's what the results look like without the alignment on the left column and their
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approach on the right.
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Of course, this process is a bit more complicated, and all the details can be found in the paper
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linked in the references.
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Note that just like most GANs implementations, their architecture needed to be trained.
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Here, they used a StyleGAN2-base network trained on the FFHQ dataset.
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Then, since they made many modifications, as we just discussed, they trained a second
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time their modified StleGAN2 network using 198 pairs of images as hairstyle transfer
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examples to optimize the model's decision for both the appearance mixture ratio and
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the structural encodings.
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Also, as you may expect, there are still some imperfections like these ones where their
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approach fails to align the segmentation masks or to reconstruct the face.Still, the results
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are extremely impressive and it is great that they are openly sharing the limitations.
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As they state in the paper, the source code for their method will be made public after
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an eventual publication of the paper.
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The link to the official GitHub repo is in the references below, hoping that it will
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be released soon.
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Thank you for watching!