One of the biggest differences is the conditioning signal. Genie 3 and similar input raw keyboard commands (WASD + arrow keys), while Atlas inputs camera poses. This small difference means that Genie 3 has no 3D whatsoever; the model needs to learn an internal mapping between keyboard commands, world states, and pixels; and with Genie 3 there is no clear way to control the generated world aside from the input image and text prompt. Since Atlas makes camera pose explicit it can use posed input frames to shape the generated world, giving you a lot more creative control.

Another big differentiator is multimodality. Genie 3 only outputs pixels. Atlas also outputs pixels, but it can also output explicit 3D for the cases where you need it (such as plugging into game engines, simulators, or VFX workflows)

Unfortunately that's a complex question... this depends on the number of diffusion steps, the size of the context, the image resolution, and the type and number of inference devices we use. There are lots of knobs to trade off speed, quality, latency, throughput, and cost.

An ideal workflow would be something semi-interactive that you can use to quickly iterate on an idea, followed by a longer offline bake-out to generate final production-quality assets.

Atlas project lead here.

Atlas is an auto-regressive diffusion model, so context length limitations apply similar to LLMs and video models.

Where Atlas has an edge is that its context comprised of an arbitrary sequence of images with camera poses, which lends itself to managing the context in creative ways (we called this "context juggling" in our RTFM blog, https://www.worldlabs.ai/blog/rtfm). So yes through clever context management you could potentially build an entire 3D model of the world.