Game artists use scanning (photogrammetry) to create ultra-realistic assets. By taking thousands of photos of a real tree from every angle, they generate a 3D model that is a direct digital copy, effectively making the in-game object a "digital ghost" of a real one.
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Historically, computer vision treated 3D reconstruction (capturing reality) and generation (creating content) as separate fields. New techniques like NeRFs are merging them, creating a unified approach where models can seamlessly move between perceiving and imagining 3D spaces. This represents a major paradigm shift.
Creating rich, interactive 3D worlds is currently so expensive it's reserved for AAA games with mass appeal. Generative spatial AI dramatically reduces this cost, paving the way for hyper-personalized 3D media for niche applications—like education or training—that were previously economically unviable.
GI discovered their world model, trained on game footage, could generate a realistic camera shake during an in-game explosion—a physical effect not part of the game's engine. This suggests the models are learning an implicit understanding of real-world physics and can generate plausible phenomena that go beyond their source material.
Large language models are insufficient for tasks requiring real-world interaction and spatial understanding, like robotics or disaster response. World models provide this missing piece by generating interactive, reason-able 3D environments. They represent a foundational shift from language-based AI to a more holistic, spatially intelligent AI.
Instead of manually designing every detail, games like Minecraft use algorithms (procedural generation) to build vast worlds. This technique, similar to natural laws, allows for emergent complexity and unique landscapes that can surprise even the game's creators, fostering a sense of discovery.
Current multimodal models shoehorn visual data into a 1D text-based sequence. True spatial intelligence is different. It requires a native 3D/4D representation to understand a world governed by physics, not just human-generated language. This is a foundational architectural shift, not an extension of LLMs.
Game engines and procedural generation, built for entertainment, now create interactive, simulated models of cities and ecosystems. These "digital twins" allow urban planners and scientists to test scenarios like climate change impacts before implementing real-world solutions.
Achieving photorealistic virtual nature requires immense computational power, leading to significant energy consumption and carbon emissions. The gaming industry's emissions are estimated to be around 50 million tons of CO2 annually, comparable to a country like Sweden, ironically harming the real environment it seeks to simulate.
When analyzing video, new generative models can create entirely new images that illustrate a described scene, rather than just pulling a direct screenshot. This allows AI to generate its own 'B-roll' or conceptual art that captures the essence of the source material.
Early games used nature as simple scenery. Later, it became a key part of gameplay. Now, in open-world games, virtual nature is a complex, living system that operates independently of the player, creating a more immersive and realistic experience.