Many years ago, we at Sanyi Life once experienced a certain brand of engineering machine at MWCS. Its biggest feature is that it is equipped with a set of ToF scanners at the front camera position, and the 3D "headmodel" of the selfie subject can be established through laser array ranging.
Then you can adjust the details of this 3D modeling to achieve operations such as "face slimming", "cheekbone shaping", "chin shaving" and so on. Finally, the mobile phone superimposes the ordinary selfie image with the "3D headform" at the corresponding angle (adjusted), so as to obtain a beauty result that is more natural than the simple "2D beautification" effect, especially the surface texture and edge lines are not easy to "goof".
At first glance, this seems like a promising feature that should be sought after by some users. But in conclusion, it has not been used in any production machine for many years after its announcement.
In fact, it is understandable, after all, the front-facing ToF sensor on Android phones is indeed so "short-lived", and it does not resonate with consumers. And as the mainstream aesthetics of smartphones have become more and more focused on "screen-to-body ratio", not only front-facing ToF, but even all designs related to front-end components have almost come to a standstill. "Gadgets" such as front multi-camera, outsole CMOS, flip camera, and even front fill light are now less and less manufacturers are willing to invest.
So does this mean that consumers no longer have the need for "selfies" and "beautification"? Judging by what's happening on social media, it's clear that this isn't the case. After all, various "beauty camera" apps still occupy the hot list, and "high P" and even photos with distorted backgrounds still represent the objective needs of many users for the function of mobile phone selfies.
Because of this, just two years ago, the brand that once made the "ToF 3D beautification" engineering machine announced their new selfie beautification system based on AIGC at a technical communication meeting.
According to the statement at the time, the new algorithm can not only 3D model the entire human body and achieve "3D human body shape adjustment", but also realize "generative beautification" of portraits based on AIGC, which can not only modify the ambient light, but also change hairstyles, expressions, and the age of the photographed faces.
Does it seem powerful? But the reality is that to this day, we have not seen any mainstream mobile phone manufacturers use such a complex AIGC algorithm for the "selfie" function.
Note that we are not saying that there are no manufacturers who use AIGC to do the camera function, for example, Xiaomi's previously released Xiaomi 14 Ultra has a built-in AIGC-based ultra-long-range zoom enhancement function. When turned on, it can call the algorithm at certain zoom focal lengths to increase the zoom detail. For example, in some of vivo's new machines, the AIGC algorithm has also been used to beautify the background of photos, such as if you want to shoot the atmosphere of winter in summer, it can be realized.
However, when it comes to applying AIGC-related technology to selfies, mobile phone manufacturers are generally cautious nowadays. Obviously, this cannot be explained simply by "technology", so what is the reason behind it?
Judging from the current information, there may be the following three factors.
One is the source of AIGC's training data. Because it is well known that the prerequisite for AIGC is to "heap" a large enough model with a sufficient amount of training data. For the demand for beautification, it is first necessary to have an AI model that knows how to "draw" all kinds of beautiful face information.
Nowadays, many image software will add hidden watermarks to avoid being used for AI large model "learning"
But obviously, in this training process, manufacturers need to use a large enough amount of face data to "feed" the AI to form its "aesthetics". So where does this facial data come from, and how does it ensure enough diversity (to prevent the final AI-generated beautification results from being "cookie-cutter") will be a big concern.
Secondly, even if the training problem of the AI beautification model is solved, the AIGC beautification algorithm is powerful enough to make it achieve the effect of "confusing the real with the fake". Then this will raise another concern, that is, whether the overly powerful beautification algorithm will make the threshold for selfie P-image fraud too low, which will bring some potential problems.
And it is precisely because generative AI is objectively too powerful and realistic "creative ability" that most of the current related software and services will actively watermark it to indicate its "unnatural" identity.
But in this way, this can have a rather embarrassing result. That is, if consumers do use "AIGC beauty", they can certainly get a photo that looks perfect, but in the eyes of others, the conspicuous (and probably full-screen, and irremoparable) "AIGC" watermark will also remind everyone that the photo is "completely fake". In this way, I believe you can also imagine the market prospect of the "AIGC beautification" function, and why it has not yet been brought to the market by mainstream manufacturers.
[The picture in this article comes from the Internet]