What happens when you give a world model 32 images of a place we know very well? World Labs put Atlas to the test on Voyager. Atlas brings text, images, video, and 3D into a shared spatial context, enabling one model to generate new views, reconstruct scenes, and simulate worlds. With Voyager, you can now see that in action in real time. Pretrained from scratch on NVIDIA Blackwell GPUs. Take a look around 👇

From 32 input images to real-time flight through @nvidia's Voyager headquarters. Trained on NVIDIA Blackwell GPUs, Atlas uses these images as 3D spatial context to generate new views, letting you explore with pixel-perfect camera control. Take a look around.

Atlas beta access is rolling out to users, some ideas for API developers - give Astra "eyes" in Blender, let it place and render new camera views with Atlas - 3D reconstruction (this video uses 9 image inputs of my studio to make a scene, any Zillow apartment works) - graybox-to-world, use depth inputs and generate multi-view renders of a scene

SparkJS 2.2.0 is out packing tons of bug fixes an improvements. Enjoy! 🎉 🥳 20% faster splat sorting, 14% FPS gains in some systems, 50% smaller bundle size and more. Dive into the full detailed change log

University leaders should make sure they reason about the broader trend. Around 2020, Fei-Fei and others at Stanford identified the growing compute divide between industry and academia, and the need for policy response in pushing for the National Research Cloud. In 2021, the same point was made when applied to model training in the foundation models paper. In 2022, the same point was made when applied to model evaluation in HELM among other works. In 2026, the same point was made when applied to model use. OpenAI spent more than $6.5 million to resolve Navier Stokes (Astra prices for 130B output tokens only). The stakes are not just the academic computer scientists doing frontier research on how to build AI models. The stakes are about the whole university system doing frontier research. Coping with "scarcity is the mother of invention" won't cut it, and we should trust the "godmother of AI" on that.

The world of R&D is forking into two paths: the token-abundant research, and the token-starved research. The future is in the former - evidentially, the progress by today's top AI industry teams and neolabs is breathtaking, where researchers’ human brilliance is super charged by AI’s assistance. Every research university president should be reading this report and reflecting on what the future of higher education research should be.

For those who remember @theworldlabs original RTFM demo: Atlas allows us to break out of these small spaces. Here I took the kids playroom and using Atlas-turbo prompted a new outside area into existence.

Our new Atlas model now runs real-time after some intense inference optimization! Generating and navigating worlds is so much more visceral when it's interactive. Inference runs on both B200 and MI355X with similar performance. Both devices are incredible workhorses!

World Labs co-founders Justin Johnson and Dr. Fei-Fei Li say LLMs use next-token prediction, but spatial intelligence has its own equivalent: Justin: "The soft definition of AI-completeness is there's this fundamental primitive that's an AI task. But if I could solve this AI task in its full, broadest generality, it would solve any intelligence problem." "The classic example in LLMs is that next-token prediction is AI-complete... I think from Ilya: there's a mystery novel, the thing has to read the whole novel, and the final sentence is, 'And the killer was.' Predict the next token. You could basically frame any kind of intelligence task in terms of that." "So clearly next-token prediction is something people believe is AI-complete." "New-view prediction, this primitive that we have in Atlas, especially generative new-view prediction, is also AI-complete." "I want to have a world where Martin is writing a proof of the Riemann hypothesis on the blackboard, and then the camera pans over to the next whiteboard." Fei-Fei: "Evolution had to solve new-viewpoint prediction by making animals move. Nature gave animals eyes, but nature didn't give trees eyes. Why? Because when you move, you see a new viewpoint... We do believe very strongly that next-viewpoint prediction is the equivalent of next-token prediction." @jcjohnss @drfeifei @martin_casado

>blogs about ray tracing for a CS project in 2012 >co-founds World Labs in 2024 >ships Atlas in 2026, a spatial intelligence model that turns a few photos into a whole 3D world curiosity compounds

World Labs co-founder Justin Johnson says Atlas can recreate the famous "Bullet Time" shot from The Matrix with iPhones: "The way they did that shot is they had a ring of hundreds of cameras. [Neo] fell over in the studio, they had hundreds of cameras viewing that angle on a green screen, and then they used those hundreds and hundreds of cameras to make that famous shot in The Matrix." "Now with Atlas, we can do this with as few as three cameras. No studio capture, no green screen, no expensive calibration. We can literally stick three iPhones on tripods and use these to take video of something happening." "From those three iPhone videos, we can then reframe the shot and imagine, like, freeze time, have the camera fly in as the milk is splashing up, and get these amazing frozen-time views. We can do this with just a couple cameras." @jcjohnss

Old footage sitting in your camera roll could now be reconstructable as a 3D scene. World Labs co-founders Ben Mildenhall and Fei-Fei Li on how Atlas got there: Ben: "In a casual sense... I took three photos of this object, or six photos of this room. I look at the photos, I can understand in my mind how those piece together. I can fill in the gaps and get it." "But there's never really been any reconciliation between those data-driven priors and the brute force dense reconstruction, which is much more akin to scientific or medical imaging... When we say dense, we really mean dense." "This room, I want like 100, 200, 300 photos to capture it. And what we're trying to do is bring that down to like three. We're saying like 50, 100x reduction." "At that scale it completely flips that calculus on its head of what type of captures you reconstruct. You can go back to existing imagery you have. You can go to stuff you find on the internet and even build scenes out of that. You can go to casual videos and unearth a lot of footage that in the past we would never have treated as reconstructable, and bring it to life as 3D." "This is something we've been playing around with a lot with Atlas. Taking old clips. I've taken a bunch of my own old captures that never worked before and put them through the system and seen a reconstruction for the first time." Fei-Fei: "The Stanford demo is underappreciated. Anywhere between 3 to 25 images, you can reconstruct that entire Stanford quad... Everything you see is generated, but according to the laws of reconstruction. And this is really magical." @BenMildenhall @drfeifei

Next view prediction is the key to Atlas, enabling us to unify pixel-level generation and reconstruction. @jcjohnss @BenMildenhall @martin_casado and I had a deeper discussion on some of the most exciting technical innovations of Atlas, our newly released world model for spatial intelligence!