Decart might not yet have the same kind of brand recognition as AI industry peers like OpenAI, Anthropic or Gemini, but its impact on the world could be just as monumental.
The AI startup is investing heavily in laying the foundations of “physical AI,” essentially a future in which autonomous robots will live and work side-by-side with humans. It’s a pioneering frontier lab that’s focused on the development of world models and real-time landscape generation, and it’s on the verge of fulfilling that vision with the recently announced release of Oasis 3.
The third generation of its world model series, Oasis 3 has dropped just weeks after Decart raised $300 million in funding from investors like Nvidia and Toyota, in a move that could position it as a vital infrastructure player at the heart of AI automation.
From generative video to world models
Oasis 3 represents a significant advance in the evolution of world models, introducing unprecedented real-time, action-conditioned video generation capabilities. It’s designed to enable the hyper-realistic simulations needed to create the heavy-duty training environments that autonomous systems need to sharpen their skills for real-world deployment.
Accessible through a live API, developers can use Oasis 3 to generate endless simulations of the realistic, physics-based real-world environments necessary to scale robotic reinforcement learning training loops.
Decart has come a long way in a very short space of time. The company’s original world models made a big splash in AI circles, but faced significant limitations that hampered their usefulness for robotics training. The first iteration of Oasis was introduced back in October 2024, earning much acclaim for its ability to quickly spin up interactive and playable sandbox environments that bore a resemblance to open-world games like Minecraft.
Remarkably, Oasis 1 didn’t generate any code to create these interactive worlds. Instead, Decart opted to use a next-frame prediction model that had been trained on millions of hours of videos of people playing computer games.
Oasis 1 was impressive, but it was mostly remarkable as a novel proof of concept. That changed with the debut of Oasis 2 in September 2025. This iteration introduced significant upgrades in areas such as frame rate stability, visual fidelity and contextual memory, allowing users to change the camera perspective and then return to their original position and see that everything was in the same place. It was proof of Decart’s ability to solve the challenge of long-term coherence.
At the same time, Decart was also putting a lot of effort into improving the realism of its generative world models. The company launched its flagship video transformation model Lucy 1 at the same time as Oasis 2’s arrival, giving users the ability to generate highly-realistic video footage and edit it in real time without any errors caused by model drift.
With the subsequent release of Lucy 2.0 earlier this year, Decart added further refinements in moves that demonstrated its growing mastery of low-latency video generation, and it’s these breakthroughs that led to the photorealistic detail now seen in Oasis 3.
Cutting-edge specs ready for training
Oasis 3 represents the culmination of Decart’s advancements in both world models and generative video streams, enabling it to deliver the high-performance specifications required to enable accurate physical AI training.
Thanks to the progress made in terms of stability, the model is now able to generate “endless” 3D worlds without any limitations on duration, and it can do so at an impressive throughput of 22 frames per second and at 768px resolution. While not quite at the level of detail of 4K video generators, this is a viable balance of efficiency and visual clarity required for robotics training pipelines.
The company has made equally impressive gains in terms of latency, which has dropped to less than 200 milliseconds, enabling robots to interact with the simulations and instantaneously receive feedback. This immediate response is what makes reinforcement training loops possible.
Some of the most notable improvements are not visual clarity or responsiveness, but in the model’s ability to accurately simulate the physics of the real world. Thanks in part to its innovative multiview camera synchronization, Oasis 3 can output three camera angles simultaneously to ensure that frames generated across each perspective are always perfectly aligned. This is critical for robots to be able to perceive depth and peripheral awareness.
Human imagination that informs physical reality
Oasis 3 is poised to unlock some serious advances in the capabilities of autonomous robots and vehicles. The Decart API makes it simple to integrate the model’s interactive generative environments into existing development pipelines and adapt them on the fly using natural language prompts.
The idea is that developers will be able to create multiple driving scenarios, introduce different weather phenomena and even hazards such as other vehicles skidding over snowdrifts and breaking down, giving the autonomous car’s underlying models the chance to learn how to deal with them.
Traditionally, the only way for developers to train AI models on these edge use cases has been to invest in recreating such scenarios in the real world, so that humans can create the realistic training data required. But such a slow and expensive approach simply cannot be scaled to any useful degree. With Oasis 3, on the other hand, it’s possible to generate an infinite number of training environments depicting just about every kind of risk one could conceivably face while out on the road.
Decart says this infrastructure can be applied to almost any kind of physical AI use case. So not just self-driving cars, but also industrial drones, off-road vehicles, autonomous boats and the kinds of unique hazards they’ll be exposed to. It’s also perfect training fodder for humanoid robots, enabling developers to imbue them with the fine motor skills needed to manipulate different kinds of objects with their bare hands.
In every case, Decart’s infrastructure promises to massively increase the output of training data required to bring physical AI from the realms of science fiction into the real world.
