Valeo Group Valeo Group | 22 Jan, 2026
| 4 min
Valeo and NATIX Partner to Build One Of the Largest Open-Source Multi-Camera World Foundation Models
The collaboration combines Valeo’s world modeling expertise with NATIX’s global multi-camera dataset to accelerate the development of Physical AI
January 22, 2026 – Paris (France)/Hamburg (Germany) – Valeo, a global leader in automotive technology, and NATIX Network, a global camera DePIN (decentralized physical infrastructure network), announce a partnership to build one of the largest open-source multi-camera World Foundation Model (WFM).
The rapid advancement of autonomous driving and robotics is creating new possibilities, driven by the growing demand for diverse, high-quality real-world data. By combining Valeo’s world models expertise and NATIX’s decentralized 360° real-world data network, the partners will build an open-source world model capable of learning, predicting, and reasoning about real-world motion and interaction.
“Since our creation in 2018, Valeo’s AI research center has been at the forefront of AI research in the automotive industry, especially in the fields of assisted and autonomous driving. Our goal has always been to advance mobility intelligence safely and responsibly,” said Marc Vrecko, Chief Executive Officer of Valeo’s Brain Division. “By combining Valeo’s generative world modeling research expertise with NATIX’s global multi-camera data, we are accelerating both the quality and the accessibility of next-generation end-to-end AI models, enabling the research community to build upon strong open models.”
“WFMs are a once-in-a-generation opportunity — similar to the rise of LLMs in 2017–2020,” said Alireza Ghods, CEO and co-founder of NATIX. “The teams that build the first scalable world models will define the foundation of the next AI wave: Physical AIs. With our distributed multi-camera network, NATIX has a clear advantage of being able to move faster than large OEMs.”
A New Foundation for Open Access Real-World Modeling
To build autonomous systems that can function in the physical world, machines must learn to understand the 4-dimensional environment, i.e., space and time. World Foundation Models (WFMs) push the boundaries of generative AI beyond text to the real world, enabling systems to reason, predict future states, and act in physical environments.
Unlike existing perception-only models, multi-camera world models anticipate what will happen next, not just what is happening now. Grounded in continuously captured, real-world multi-camera data, the Valeo–NATIX approach enables AI to learn from true edge cases and accelerates the safe deployment of autonomous systems.
Developed under an open-source framework, the Valeo–NATIX approach will release models, datasets, and training tools openly, enabling developers to fine-tune world models and benchmark Physical AI across regions and driving conditions. This collaboration builds on Valeo’s VaViM (Video Autoregressive Model) and VaVAM (Video- Action Model), two open-source frameworks trained mainly on front-camera video, including large-scale online datasets. NATIX complements this with its multi-camera network, which has collected over 100k hours of multi-camera driving data (600K hours of video data) in 7 months, and continuously captures such data from real vehicles across the US, Europe, and Asia. Extending world models from front-view to multi-camera inputs gives AI the same complete spatial perception that autonomous vehicles and robots use in practice.
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