Rather than needing to draw out every element of an imagined scene, users can enter a brief phrase to quickly generate the key features and theme of an image, such as a snow-capped mountain range. This starting point can then be customized with sketches to make a specific mountain taller or add a couple trees in the foreground, or clouds in the sky.
It doesn’t just create realistic images — artists can also use the demo to depict otherworldly landscapes.
Today we are introducing Pet Portraits, a way for your dog, cat, fish, bird, reptile, horse, or rabbit to discover their very own art doubles among tens of thousands of works from partner institutions around the world. Your animal companion could be matched with ancient Egyptian figurines, vibrant Mexican street art, serene Chinese watercolors, and more. Just open the rainbow camera tab in the free Google Arts & Culture app for Android and iOS to get started and find out if your pet’s look-alikes are as fun as some of our favorite animal companions and their matches.
In traditional graphics work, vectorizing a bitmap image produces a bunch of points & lines that the computer then renders as pixels, producing something that approximates the original. Generally there’s a trade-off between editability (relatively few points, requiring a lot of visual simplification, but easy to see & manipulate) and fidelity (tons of points, high fidelity, but heavy & hard to edit).
Importing images into a generative adversarial network (GAN) works in a similar way: pixels are converted into vectors which are then re-rendered as pixels—and guess what, it’s a generally lossy process where fidelity & editability often conflict. When the importer tries to come up with a reasonable set of vectors that fit the entire face, it’s easy to end up with weird-looking results. Additionally, changing one attribute (e.g. eyebrows) may cause changes to others (e.g. hairline). I saw a case once where making someone look another direction caused them to grow a goatee (!).
My teammates’ FaceStudio effort proposes to address this problem by sidestepping the challenge of fitting the entire face, instead letting you broadly select a region and edit just that. Check it out:
Leaving aside the eye-popping, sometimes disconcerting applications of GANs for facial synthesis & editing, what if the core tech could be used just to generate high-quality results even with poor bandwidth? That’s one possible application of NVIDIA’s recent endeavors:
By analyzing various artists’ distinctive treatment of facial geometry, researchers in Israel devised a way to render images with both their painterly styles (brush strokes, texture, palette, etc.) and shape. Here’s a great six-minute overview:
What if Photoshop’s breakthrough Smart Portrait, which debuted at MAX last year, could work over time?
One may think this is an easy task as all that is needed is to apply Smart Portrait for every frame in the video. Not only is this tedious, but also visually unappealing due to lack of temporal consistency.
In Project Morpheus, we are building a powerful video face editing technology that can modify someone’s appearance in an automated manner, with smooth and consistent results.
It’s that thing where you wake up, see some exciting research, tab over to Slack to share it with your team—and then notice that the work is from your teammates. 😝
Check out StyleAlign from my teammate Eli Shechtman & collaborators. Among other things, they’ve discovered interesting, useful correspondences in ML models for very different kinds of objects:
We find that the child model’s latent spaces are semantically aligned with those of the parent, inheriting incredibly rich semantics, even for distant data domains such as human faces and churches. Second, equipped with this better understanding, we leverage aligned models to solve a diverse set of tasks. In addition to image translation, we demonstrate fully automatic cross-domain image morphing
Here’s a little taste of what it enables:
And to save you the trouble of looking up the afore-referenced Ghostbusters line, here ya go. 👻
The visualizations for StyleNeRF tech are more than a little trippy, but the fundamental idea—that generative adversarial networks (GANs) can enable 3D control over 2D faces and other objects—is exciting. Here’s an oddly soundtracked peek:
And here’s a look at the realtime editing experience:
The new 10-episode Snap original series “The Me and You Show” taps into Snapchat’s Cameos — a feature that uses a kind of deepfake technology to insert someone’s face into a scene. Using Cameos, the show makes you the lead actor in comedy skits alongside one of your best friends by uploading a couple of selfies. […]
The Cameos feature is based on tech developed by AI Factory, a startup developing image and video recognition, analysis and processing technology that Snap acquired in 2019. […]
According to Snap, more than 44 million Snapchat users engage with Cameos on a weekly and more than 16 million share Cameos with their friends.
I dunno—to my eye the results look like a less charming version of the old JibJab templates that were hot 20 years ago, but I’m 30 years older than the Snapchat core demographic, so what do I know?
These can be made with any still photo and will animate the head while other parts stay static and can’t have replaced backgrounds. Still, the result below shows how movements and facial expressions performed by the real person are seamlessly added to a still photograph. The human can act as a sort of puppeteer of the still photo image.
I keep meaning to pour one out for my nearly-dead homie, Photoshop 3D (post to follow, maybe). We launched it back in 2007 thinking that widespread depth capture was right around the corner. But “Being early is the same as being wrong,” as Marc Andreessen says, and we were off by a decade (before iPhones started putting depth maps into images).
Now, though, the world is evolving further, and researchers are enabling apps to perceive depth even in traditional 2D images—no special capture required. Check out what my colleagues have been doing together with university collaborators:
On the reasonable chance that you’re interested in my work, you might want to bookmark (or at least watch) this one. Two-Minute Papers shows how NVIDIA’s StyleGAN research (which underlies Photoshop’s Smart Portrait Neural Filter) has been evolving, recently being upgraded with Alias-Free GAN (which very nicely reduces funky artifacts—e.g. a “sticky beard” and “boiling” regions (hair, etc.):
Side note: I continue to find the presenter’s enthusiasm utterly infectious: “Imagine saying that to someone 20 years ago. You would end up in a madhouse!” and “Holy mother of papers!”
Hmm—I’m not sure what to think about this & would welcome your thoughts. Promising to “Give people an idea of your appearance, while still protecting your true identity,” this Anonymizer service will take in your image, then generate multiple faces that vaguely approximate your characteristics:
Here’s what it made for me:
I find the results impressive but a touch eerie, and as I say, I’m not sure how to feel. Is this something you’d find useful (vs., say, just using something other than a photograph as your avatar)?
You might remember the portrait relighting features that launched on Google Pixel devices last year, leveraging some earlier research. Now a number of my former Google colleagues have created a new method for figuring out how a portrait is lit, then imposing new light sources in order to help it blend into new environments.
Two-Minute Papers has put together a nice, accessible summary of how it works:
Heh—I was amused to hear generative apps’ renderings of human faces—often eerie, sometimes upsetting—described as turning people into “rotten fruits.”
This reminded me of a recurring sketch from Conan O’Brien’s early work, which featured literal rotting fruit acting out famous films—e.g. Apocalypse Now, with Francis Ford Coppola sitting there to watch:
No, I don’t know what this has to do with anything—except now I want to try typing “rotting fruit” plus maybe “napalm in the morning” into a generative engine just to see what happens. The horror… the horror!
In the magical, frequently bizarre world of generative adversarial networks, changing one attribute will often accidentally affect other “entangled” ones (e.g. I’ve seen a change of gaze cause people to grow beards!). This new tech promises better isolation of—and thus control over—things like hair style, lighting, skin tone, and more.
Okay, this one is a little “inside baseball,” but I’m glad to see more progress using GANs to transfer visual styles among images. Check it out:
The current state-of-the-art in neural style transfer uses a technique called Adaptive Instance Normalization (AdaIN), which transfers the statistical properties of style features to a content image, and can transfer an infinite number of styles in real time. However, AdaIN is a global operation, and thus local geometric structures in the style image are often ignored during the transfer. We propose Adaptive convolutions; a generic extension of AdaIN, which allows for the simultaneous transfer of both statistical and structural styles in real time.
I returned to Adobe specifically to help cutting-edge creators like this bring their magic to as many people as possible, and I’m really excited to see what we can do together. (Suggestions are welcome. 😌🔥)
OMG—I’m away from our brick piles & thus can’t yet try this myself, but I can’t wait to take it for a spin. As PetaPixel explains:
If you have a giant pile of LEGO bricks and are in need of ideas on what to build, Brickit is an amazing app that was made just for you. It uses a powerful AI camera to rapidly scan your LEGO bricks and then suggest fun little projects you can build with what you have.
Here’s a short 30-second demo showing how the app works — prepare to have your mind blown:
“A nuclear-powered pencil”: that’s how someone recently described ArtBreeder, and the phrase comes to mind for NVIDIA Canvas, a new prototype app you can download (provided you have Windows & beefy GPU) and use to draw in some trippy new ways:
Paint simple shapes and lines with a palette of real world materials, like grass or clouds. Then, in real-time, our revolutionary AI model fills the screen with show-stopping results.
Don’t like what you see? Swap a material, changing snow to grass, and watch as the entire image changes from a winter wonderland to a tropical paradise. The creative possibilities are endless.
I’m not sure whether the demo animation does the idea justice, as you might reasonably think “Why would I want to scarify a face & then make a computer fill in the gaps?,” but the underlying idea (that the computer can smartly fill holes based on understanding the real-world structure of a scene) seems super compelling.
Photoshop Neural Filters are insanely cool, but right now adjusting any parameter generally takes a number of seconds of calculation. To make things more interactive, of my teammates are collaborating with university researchers on an approach that couples cheap-n’-cheerful quality for interactive preview with nicer-but-slower calculation of final results. This is all a work in progress, and I can’t say if/when these techniques will ship in real products, but I’m very glad to see the progress.
Watch how this new tech is able to move & blend just parts of an image (e.g. hair) while preserving others:
We propose a novel latent space for image blending which is better at preserving detail and encoding spatial information, and propose a new GAN-embedding algorithm which is able to slightly modify images to conform to a common segmentation mask.
Our novel representation enables the transfer of the visual properties from multiple reference images including specific details such as moles and wrinkles, and because we do image blending in a latent-space we are able to synthesize images that are coherent.
A few weeks ago I mentionedToonify, an online app that can render your picture in a variety of cartoon styles. Researchers are busily cranking away to improve upon it, and the new AgileGAN promises better results & the ability to train models via just a few inputs:
Our approach provides greater agility in creating high quality and high resolution (1024×1024) portrait stylization models, requiring only a limited number of style exemplars (∼100) and short training time (∼1 hour).
We introduce HuMoR: a 3D Human Motion Model for Robust Estimation of temporal pose and shape. Though substantial progress has been made in estimating 3D human motion and shape from dynamic observations, recovering plausible pose sequences in the presence of noise and occlusions remains a challenge. For this purpose, we propose an expressive generative model in the form of a conditional variational autoencoder, which learns a distribution of the change in pose at each step of a motion sequence. Furthermore, we introduce a flexible optimization-based approach that leverages HuMoR as a motion prior to robustly estimate plausible pose and shape from ambiguous observations. Through extensive evaluations, we demonstrate that our model generalizes to diverse motions and body shapes after training on a large motion capture dataset, and enables motion reconstruction from multiple input modalities including 3D keypoints and RGB(-D) videos.
Erik Härkönen recently interned at Adobe, collaborating with several of my teammates on interesting emerging tech. This was all a pleasant surprise to me, as I’d independently stumbled across this fun vid in which he encapsulates some exciting things AI is learning to do with photos:
Years ago my friend Matthew Richmond (Chopping Block founder, now at Adobe) would speak admiringly of “math-rock kids” who could tinker with code to expand the bounds of the creative world. That phrase came to mind seeing this lovely little exploration from Derrick Schultz:
NVIDIA Research is revving up a new deep learning engine that creates 3D object models from standard 2D images — and can bring iconic cars like the Knight Rider’s AI-powered KITT to life — in NVIDIA Omniverse.
A single photo of a car, for example, could be turned into a 3D model that can drive around a virtual scene, complete with realistic headlights, tail lights and blinkers.
“The extraction of facial data” — a time-consuming computational process — “runs parallel with the production itself.” The technology strips actors’ faces off, converting their visages into a 3D model, according to Lynes. “This creates millions of 3D models, which the AI uses as reference points,” he says.
“And then, using an existing foreign-language recording of the dialogue, it studies the actor and generates a new 3D model per frame,” he adds. Finally, the imagery is converted back to 2D. Digital effects artists can then manually fix anything that seems off.
I’ve obviously been talking a ton about the crazy-powerful, sometimes eerie StyleGAN2 technology. Here’s a case of generative artist Mario Klingemann wiring visuals to characteristics of music:
Watch it at 1/4 speed if you really want to freak yourself out.
Beats-to-visuals gives me an excuse to dig up & reshare Michel Gondry’s brilliant old Chemical Brothers video that associated elements like bridges, posts, and train cars with the various instruments at play:
Back to Mario: he’s also been making weirdly bleak image descriptions using CLIP (the same model we’ve explored using to generate faces via text). I congratulated him on making a robot sound like Werner Herzog. 🙃
As industries evolve, core infrastructure gets built and commoditized, and differentiation moves up the hierarchy of needs from basic functionality to non-basic functionality, to design, and even to fashion.
For example, there was a time when chief buying concerns included how well a watch might tell time and how durable a pair of jeans was.
Now apps like FaceTune deliver what used to be Photoshop-only levels of power to millions of people, and Runway ML promises to let you just type words to select & track objects in video—using just a Web browser. 👀
As I wrote many years ago, it was the chance to work with alpha geeks that drew me to Adobe:
When I first encountered the LiveMotion team, I heard that engineer Chris Prosser had built himself a car MP3 player (this was a couple of years before the iPod). Evidently he’d disassembled an old Pentium 90, stuck it in his trunk, connected it to the glovebox with some Ethernet cable, added a little LCD track readout, and written a Java Telnet app for synching the machine with his laptop. Okay, I thought, I don’t want to do that, but I’d like to hijack the brains of someone who could.
Now my new teammate Cameron Smith has spent a weekend wiring MIDI hardware to StyleGAN to control facial synthesis & modification:
This stuff makes my head spin around—and not just because the demo depicts heads spinning around!
You might remember the portrait relighting features that launched on Google Pixel devices last year, leveraging some earlier research. Now a number of my former Google colleagues have created a new method for figuring out how a portrait is lit, then imposing new light sources in order to help it blend into new environments. Check it out:
Check out how StyleMapGAN (paper, PDF, code) enables combinations of human & animal faces, vehicles, buildings, and more. Unlike simple copy-paste-blend, this technique permits interactive morphing between source & target pixels:
From the authors, a bit about what’s going on here:
Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from i) time-consuming optimization for projecting real images to the latent vectors, ii) or inaccurate embedding through an encoder. We propose StyleMapGAN: the intermediate latent space has spatial dimensions, and a spatially variant modulation replaces AdaIN. It makes the embedding through an encoder more accurate than existing optimization-based methods while maintaining the properties of GANs. Experimental results demonstrate that our method significantly outperforms state-of-the-art models in various image manipulation tasks such as local editing and image interpolation. Last but not least, conventional editing methods on GANs are still valid on our StyleMapGAN. Source code is available at https://github.com/naver-ai/StyleMapGAN.
“VOGUE: Try-On by StyleGAN,” from my former Google colleague Ira Kemelmacher-Shlizerman & her team, promises to synthesize photorealistic clothing & automatically apply it to a range of body shapes (leveraging the same StyleGAN foundation that my new teammates are using to build images via text):
Artbreeder is a trippy project that lets you “simply keep selecting the most interesting image to discover totally new images. Infinitely new random ‘children’ are made from each image. Artbreeder turns the simple act of exploration into creativity.” Check out interactive remixing:
I find this emerging space so fascinating. Check out how Toonify.photos (which you can use for free, or at high quality for a very modest fee) can turn one’s image into a cartoon character. It leverages training data based on iconic illustration styles:
I also chuckled at this illustration from the video above, as it endeavors to how two networks (the “adversaries” in “Generative Adversarial Network”) attempt, respectively, to fool the other with output & to avoid being fooled. Check out more details in the accompanying article.
You say “work with an AI to make art, purely from a text prompt,” I hear “monkey with a revolver”—which reminds me, I should plug “monkey with a revolver” into this system to see what comes out. Meanwhile, example weirdness: