월드랩스, 텍스트·이미지·비디오·3D 통합 월드 모델 '아틀라스' 공개

World models generate, reconstruct, and simulate any possible world. They understand how worlds appear, behave, and evolve so that we can render imagined worlds for creative users, simulate the real world in high fidelity, and help robots plan actions. At World Labs, we build these general purpose world models in pursuit of spatial intelligence. Today we are introducing Atlas, our next-generation world model. Atlas is an omni model that we pretrained from scratch to natively operate on text, images, video, and 3D. It is a multimodal autoregressive diffusion transformer: all inputs are combined into a shared spatial context. Atlas uses that context to generate what comes next, staying consistent in 3D with everything it has seen and imagining what lies beyond it. Atlas is built to scale: its performance improves with increased training compute, and we expect this trend to hold as we continue scaling. Atlas can perform a broad range of tasks spanning world generation, reconstruction, and simulation: Camera-Controlled Generation : Atlas generates images and videos from one or more images with pixel-perfect camera control, outputting up to 1 minute of video at 1440p. Spatial Reconstruction : Atlas reconstructs real world scenes from one to dozens of input images. It generates both image frames from novel views and explicit 3D outputs, outperforming state-of-the-art models specialized for 3D reconstruction. Space-Time Simulation : Atlas models space and time from input videos, reframing videos for dramatic visual effects and enabling Real-to-Sim workflows for robotics. Image Generation : Atlas generates images and 360 panoramas from text; it can follow complex prompts, render text, and generate a wide variety of visual styles. Atlas will power future versions of Marble and other products from World Labs. Request early access to Atlas Camera-Controlled Generation Atlas takes one or more reference images and generates new views at any camera position and angle you specify. Generated views match the content and geometry of the input images, smoothly extrapolating beyond them to imagine parts of the scene not visible in the inputs. Atlas handles a broad range of scene types, visual styles, and camera motions. Pixel-Perfect Camera Control Atlas uses precise camera geometry as a native input type, going beyond coarse text-based instructions for camera control. This lets you frame every shot and control every motion. In the examples here, Atlas generates a complete scene from a single input image . It uses the content of the input image along with its broad world knowledge to imagine what the scene should look like from new angles. For example, it generates the back side of the robot, and it guesses that there should be a grassy lawn next to the pool. Generating with Spatial Context Similar to an LLM, Atlas first encodes its inputs into a context, then generates outputs conditioned on the context. However Atlas is unique because each image is grounded at a 3D position in space; this forms a spatial context . Managing this spatial context unlocks entirely new kinds of creative control. For example, you can place two unrelated reference images in the context, position them in 3D space, and Atlas generates a world that smoothly interpolates between them. These examples demonstrate the model's world knowledge and creativity; it imagines doorways, hallways, nooks, and other transitions between otherwise unrelated input images. Controllable Long Videos Atlas lets you generate long videos with precise control by combining camera movement and spatial context management. You design every scene and every camera angle. This puts you in the director's chair: you are staging the scene, not pulling the lever of a slot machine. In the example below, we generate a 1 minute video at 1440p resolution using a small number of reference images. We hand-design a camera path through the scene, and Atlas generates a coherent world. The rest of the videos on this page have been compressed to optimize page performance. Spatial Reconstruction Atlas reconstructs real-world spaces from one or more input images. It doesn't require special capture equipment or hundreds of dense views to faithfully reconstruct objects and scenes. We believe Atlas is a major step forward toward solving the problem of novel view synthesis from sparse input images, a decades-old fundamental problem in 3D computer vision. Reconstructing from Multiple Images Atlas can take a variable number of input views of a scene. When parts of the world are not visible in the input views, Atlas imagines a plausible way to fill in the gaps by drawing from its rich world knowledge. But sometimes you don't want imagination; you might want an exact reconstruction of a real-world location. Passing more input images gives Atlas more context: the more it sees, the less it imagines. Atlas typically gives faithful reconstructions with as few as two or three images, outperforming state-of-the-art results by models specially trained only for 3D reconstruction. However, Atlas can also make use of over a hundred input images in its spatial context, allowing for faithful recreation of real world environments. In the first example below, Atlas generates an aerial view of the scene from just a single ground-level photo. The garden visible in the single input photo is accurately recreated in the model output, but the rest of the scene is imagined. After adding a second real-world input image of the cottage next to the garden, the model's output shows both the garden and the cottage, but it still imagines the house to the left. After adding a third input image of the main house, the entire scene is accurately depicted. In the second example, we build up Stanford's Main Quad piece by piece, beginning with the grassy main entrance and ending with the colorful mosaics decorating the facade of Memorial Church. Though Atlas only receives two to twenty-five ground-level input images, it can generate paths from aerial views flying far above the campus. Reconstructing Diverse Paths Atlas can generate many different trajectories through the same scene, giving new perspectives on the same input images. No matter how many times you change the camera path, the scene stays consistent. In the example below, we show that given a small set of input images, Atlas can generate multiple camera paths through the same scene. Different camera paths can emphasize various parts of the scene, or change moods by varying in speed, length, or complexity. Explicit 3D Outputs In the results above you have seen Atlas output 2D images and videos, which are sufficient for some applications. But workflows in robotics, gaming, design, VFX and beyond often require explicit 3D outputs. Atlas natively operates on both 2D image frames and 3D depth maps, enabling it to output worlds as point clouds or 3D Gaussian splats. From a single input image, Atlas produces a full 3D world by jointly generating new views and estimating their geometry. From a video of a real space, it predicts the depth of every frame and combines them into a 3D reconstruction. In either case, Atlas fills in regions that no camera ever saw. Point clouds estimate a scene's geometry, but 3D Gaussian splats make it usable. Atlas fills the remaining gaps and turns the point cloud into a complete splat scene that renders on-device at high resolution and framerates. This is the same representation used in Marble , enabling Atlas to integrate naturally with the rest of our products. Space-Time Simulation Atlas serves as a world simulator. It understands both the spatial structure of the world and how the world evolves over time. Combining its spatial and temporal abilities leads to new applications for VFX, robotics, and beyond. Reframing Video Atlas turns a handful of ordinary cameras into a "bullet time" multiview capture studio. With footage from as few as three cameras, Atlas can