World Labs 发布 Atlas：一个仅凭几张照片即可生成、重建并模拟 3D 世界的单一 AI 模型

World Labs, co-founded by AI researcher Fei-Fei Li, has announced Atlas, a world model that generates, reconstructs, and simulates 3D scenes from just a few images. The company claims it beats specialized models at their own tasks, which could make many of them unnecessary. Since its founding, World Labs has pursued the goal of "spatial intelligence" , the idea that AI should understand 3D space the way humans do. Atlas is the company's first model built to do that at scale. Rather than producing flat images or video clips, it grasps how a scene looks from any angle and how it changes over time. World Labs describes Atlas as an omni-model trained from scratch on text, images, video, and 3D data. Every input gets anchored to a specific position in 3D space rather than processed as a flat sequence. The company calls this shared spatial understanding "spatial context," and it's what the model uses to generate each new frame or viewpoint. According to World Labs, this anchoring separates Atlas from pure language or video models. Fei-Fei Li laid out this exact problem in a November 2025 essay. Current multimodal language models and video diffusion models break data into one- or two-dimensional sequences, she argued, which makes even simple spatial tasks needlessly hard. What's needed are architectures that organize tokenization, context, and memory in a 3D- or 4D-aware way. For camera-controlled generation, Atlas takes one or more images and produces new views at freely chosen camera positions and angles. Camera movement is passed as a direct geometric input rather than described through text prompts, as many video models require. The model outputs up to one minute of video at 1440p. Users can control every shot themselves instead of "pulling the lever on a slot machine," as World Labs put it, drawing a line between controlled generation and random output. For spatial reconstruction, Atlas rebuilds real scenes from as few as one to several dozen input images without special capture equipment. The more images it receives, the less it has to fill in from its own knowledge. With just two or three images, Atlas delivers faithful results and outperforms specialized 3D models, according to World Labs. It can also handle over a hundred inputs. In one demo, the model progressively assembles Stanford's Main Quad from two to 25 ground-level photos and generates aerial views far above the campus. This is where existing models tend to fall apart. In a comparison within the OpenWorldLib framework , systems like VGGT and InfiniteVGGT showed geometric inconsistencies and blurry textures as soon as the camera moved significantly. Atlas can output results as actual 3D data, not just images or video, because it processes depth information alongside RGB. Supported formats include point clouds and 3D Gaussian splats , which build a scene from many small spatial data points that can be viewed smoothly from any angle. This matches the representation used in Marble, the company's existing product . As a simulator, Atlas models space and time together. From footage captured by just a few cameras, it can produce a "bullet time" effect that freezes a scene and lets users view it from otherwise impossible angles. The demo footage was shot with a handful of smartphones and action cameras, not professional gear. For robotics, Atlas serves as a real-to-sim tool. It reconstructs a room and generates the image and depth data that a simulated robot's sensors would see along its path. From just a few photos, users can simulate and vary grasping and movement tasks by swapping out objects, positions, lighting, or backgrounds. The goal is to produce diverse training data for robots without capturing every situation in the real world.