Decart’s new world model can simulate hours of photorealistic driving — with some caveats
Decart has released Oasis 3, the latest iteration of its real-time world model, which is built to simulate photorealistic driving environments over extended periods. The system is aimed primarily at autonomous vehicle development, where the ability to generate diverse, high-fidelity road scenarios without physical test drives can significantly reduce both cost and risk. The model is now available through an API, allowing developers and research teams to integrate it into their own workflows and tooling.
World models like Oasis 3 work by learning the visual and physical dynamics of an environment from large datasets, then generating coherent, continuous video output that responds to inputs - in this case, the kinds of conditions and decisions a self-driving system might encounter. The promise is that a model trained on real driving footage can stand in for real-world testing across a wide range of edge cases, from unusual weather to complex intersections, without requiring a vehicle on the road.
The "some caveats" noted in the announcement are worth paying attention to. Sustained photorealistic coherence over hours of simulated driving is a hard problem - world models tend to accumulate errors or visual drift the longer they run, and there are open questions about how faithfully they reproduce the long-tail scenarios that matter most for safety validation. Whether Oasis 3 has meaningfully closed that gap, or whether the hours-long capability comes with constraints on scene complexity or interactivity, will likely depend on how developers stress-test it in practice.
Decart previously drew attention with an earlier version of Oasis focused on interactive game-world generation, making the pivot toward automotive simulation a notable shift in focus. Positioning Oasis 3 as an API product suggests the company is targeting enterprise and research users rather than consumers, and it places them in a competitive space alongside other groups working on generative simulation for robotics and autonomous systems. How the model holds up under rigorous AV development requirements - where the bar for reliability is high - remains to be seen.
