Title: Video models are zero-shot learners and reasoners
Primary area: generative models
Keywords: video models, zero-shot learning, visual reasoning, foundation models

Abstract: The remarkable zero-shot capabilities of Large Language Models (LLMs) have propelled natural language processing from task-specific models to unified, generalist foundation models. This transformation emerged from simple primitives: large, generative models trained on web-scale data. Curiously, the same primitives apply to today’s generative video models. Could video models be on a trajectory towards general-purpose vision understanding, much like LLMs developed general-purpose language understanding? We demonstrate that Veo 3 can solve a broad variety of tasks it wasn’t explicitly trained for: segmenting objects, detecting edges, editing images, understanding physical properties, recognizing object affordances, simulating tool use, and more. These abilities to perceive, model, and manipulate the visual world enable early forms of visual reasoning like maze and symmetry solving. Veo’s emergent zero-shot capabilities indicate that video models are on a path to becoming unified, generalist vision foundation models.

TL;DR: Veo 3 (a large video model) shows emergent zero-shot abilities across many different visual tasks, indicating that video models are on a path to becoming unified foundation models for vision just like LLMs became foundation models for language.