Excited to share this demo with the HN.
All of the portraits in this demo are computer-generated by a machine learning model called “StyleGAN”. While most of the recent excitement around StyleGAN centers around its amazing ability to generate infinite variation (e.g. thispersondoesnotexist.com <3), the emergent semantics encoded in the latent space are impressive as well.
For instance, faces in this space allow for some semantic vector math, reminiscent of word2vec’s “king - man + woman = queen” (https://p.migdal.pl/2017/01/06/king-man-woman-queen-why.html).
We can find the latent representations of, say, smiling people. We can then average them and create a new semantic vector that, when added to pictures of non-smiling faces, makes them all smile.
Play with the sliders to see what I mean.
Some possible applications: Generation of assets for games, Customizing ad photography by region/demographics, Lifelike, custom avatars, Compression, Modeling longitudinal medical imagery, Zero-shot inpainting, super-resolution, etc.
Happy to answer any questions!