Video models are zero-shot learners and reasoners
Authors: name: Thaddäus Wiedemer, name: Yuxuan Li, name: Paul Vicol, name: Shixiang Shane Gu, name: Nick Matarese, name: Kevin Swersky, name: Been Kim, name: Priyank Jaini, name: Robert Geirhos
Organization: Deepmind

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.