World Labs / Spatial Intelligence

World Labs is an AI company founded in 2024 by Fei-Fei Li, the Stanford professor widely regarded as a pioneer of modern computer vision and co-creator of ImageNet. The company focuses on developing spatial intelligence — the ability for AI systems to perceive, reason about, and interact within three-dimensional environments through internal world models.¹⁾ World Labs builds Large World Models (LWMs) that represent 3D space, track object positions and relationships, and enable prediction, planning, and action in real or simulated environments.²⁾

Spatial intelligence refers to AI's capacity to build and maintain internal representations of 3D space that integrate:

• Perception — recovering 3D structure from 2D images and sensor data (depth estimation, object detection, segmentation)
• Geometry — understanding shapes, surfaces, volumes, and spatial relationships between objects
• Memory — tracking how objects, agents, and environments change over time
• Action — using spatial understanding to plan and execute physical interactions

Humans naturally think spatially — we predict collisions, navigate cluttered rooms, and imagine how objects fit together. Current AI systems, largely trained on 2D images and text, lack this fundamental capability.³⁾

LWMs are dynamic internal maps of 3D environments that:

• Track objects and their 3D positions, orientations, and relationships
• Maintain consistency across viewpoints and over time
• Predict how scenes will change in response to actions or physical forces
• Enable planning by simulating future states before committing to actions

Unlike label-based 2D systems that classify images, LWMs create coherent, physics-aware representations suitable for imagination, prediction, and interaction.⁴⁾

• Neural Radiance Fields (NeRF) — representing 3D scenes as continuous neural functions that can synthesize novel viewpoints
• 3D Scene Graphs — structured representations of objects, their attributes, and spatial relationships⁵⁾
• Depth Estimation — inferring 3D depth from monocular or stereo images
• Vision-Language Models — combining visual perception with language understanding for spatial reasoning about layouts, navigability, and functional spaces
• Gaussian Splatting — efficient 3D representation for real-time rendering and scene reconstruction

Spatial intelligence enables robots to navigate cluttered environments, manipulate objects, collaborate with humans, and understand functional spaces (e.g., where a cup can be placed vs. where it will fall).⁶⁾

LWMs support object placement in AR scenes, virtual tours with navigable 3D environments, and immersive content creation that respects real-world physics and spatial constraints.

Self-driving vehicles, delivery drones, and warehouse robots require real-time spatial reasoning to navigate safely in dynamic environments.

Creating accurate 3D digital replicas of physical spaces for training AI, testing scenarios, and optimizing real-world operations.

• Fei-Fei Li (Founder) — Sequoia Professor of Computer Science at Stanford, co-director of the Stanford Human-Centered AI Institute (HAI), known for ImageNet and advancing computer vision
• The team draws from researchers at Stanford and leading institutions working on 3D world models, embodied AI, and neural scene representations
• World Labs raised significant early-stage funding in 2024, positioning itself at the frontier of spatial AI research and commercialization

Current AI models still struggle with complex 3D spatial reasoning tasks, including understanding occlusion, physical plausibility, and multi-object interactions in novel scenes.⁷⁾ Building LWMs that generalize across diverse environments remains an open research problem.

• Computer Vision
• Embodied AI
• Neural Radiance Fields (NeRF)