• hese scaling laws have been identified as a characteristic pattern in the performance and capabilities of large language models. Despite the shift in the domain from text-based language tasks to video modelling, GAIA-1 exhibits analogous trends. This suggests that as GAIA-1’s model size and training data scale up, its proficiency and performance in video generation tasks continue to improve, mirroring the scalability trends observed in large language models when applied to their respective domains. In essence, GAIA-1’s world modelling task, focused on the next token prediction within the context of videos, shares the scaling behaviours that have become a hallmark of large language models in the realm of text and language tasks. This underscores the broader applicability of scaling principles in modern AI models across diverse domains, including autonomous dri (View Highlight)
  • GAIA-1 introduces a novel approach to generative world models in the context of autonomous driving. Our research showcases the potential of multi-modal learning, integrating video, text, and action inputs to create diverse driving scenarios. GAIA-1 stands out for its ability to provide fine-grained control over ego-vehicle behaviour and scene elements, enhancing its versatility in autonomous system development. GAIA-1 uses vector quantised representations to reframe the future prediction task into a next-token prediction problem, a technique commonly employed in Language Models (LLMs). GAIA-1 has shown promise in its ability to comprehend various aspects of the world, such as distinguishing between objects like cars, trucks, buses, pedestrians, cyclists, road layouts, buildings, and traffic lights. Additionally, GAIA-1 utilises video diffusion models to generate more visually realistic driving scenes. (View Highlight)