Google Allo Gaining Personalized Sticker Packs Generated Through Neural Network-Powered Selfies

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Google this week announced an update coming soon to its iOS Google Allo app, which will introduce neural network-generated stickers that are personalized to match your own distinct look. The company said that the new feature will combine neural networks and the work of artists to turn your selfies into unique sticker packs, which can then be used in Allo, the company's messaging app.

The process to generate the stickers is relatively simple, according to Google, with the sticker packs of your likeness being created "on the fly" after you take a selfie. If you aren't happy with some aspects of the stickers, a few customization options will appear so you can tweak and personalize even more. You can take a selfie with a few filters for a more comical sticker pack, or opt for results that more closely resemble you.


Google went deeper into the process of developing the neural network for the new sticker pack feature in Allo, as well as how the collaborative artists working with the company affected the end results. The pack that will debut at launch is said to be the first in a line of artistic styles -- Google said it "speaks to your sarcastic side" -- and future updates will add different emotive artwork that "might be more cute for those sincere moments."

The first pack was designed by artist Lamar Abrams, who works as a story boarder on Steven Universe, and whose feature designs for Allo help account for more than 563 quadrillion potential combinations of personal stickers.

To create an illustration of you that captures the qualities that would make it recognizable to your friends, we worked alongside an artistic team to create illustrations that represented a wide variety of features. Artists initially designed a set of hairstyles, for example, that they thought would be representative, and with the help of human raters we used these hairstyles to train the network to match the right illustration to the right selfie.

We then asked human raters to judge the sticker output against the input image to see how well it did. In some instances, they determined that some styles were not well represented, so the artists created more that the neural network could learn to identify as well.

Google Allo launched last year and combined standard messaging threads, photo editing, and stickers with the company's AI helper Google Assistant. The AI could be brought up directly within a chat thread to find somewhere nearby to eat (even by using related emojis, like the taco), search for a YouTube video, and more.

A few of the 563 quadrillion combinations that can come from Abrams' basic feature illustrations

Google didn't confirm when the new neural network update would appear on Google Allo for iOS [Direct Link], but Android users can use the selfie feature right now.

Tags: Google, Allo

Top Rated Comments

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Avatar
43 months ago
Another solution in search of a problem...
Score: 7 Votes (Like | Disagree)
Avatar
43 months ago
I really don't understand this sticker trend. Why not just type out what you want to say? Eh, maybe it's just me.
Score: 5 Votes (Like | Disagree)
Avatar
43 months ago
How can Google of all software companies, make terrible apps and then keep supporting them? Is this what a big company bureaucracy looks like?

Hangouts and Voice are terrible, I always scratch my head the few times I have to use them.
Score: 5 Votes (Like | Disagree)
Avatar
43 months ago
"The years passed, mankind became stupider at a frightening rate. Some had high hopes the genetic engineering would correct this trend in evolution, but sadly the greatest minds and resources where focused on conquering hair loss and prolonging erections."
Score: 4 Votes (Like | Disagree)
Avatar
43 months ago
The amount of uses for this isn't 0, it's a negative number.
Score: 3 Votes (Like | Disagree)
Avatar
43 months ago
I don't know what's worse, trying to hype something so lame by using the buzzword "neural", or that the results are so hideously ugly.
Score: 2 Votes (Like | Disagree)

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