Video models are zero-shot learners and reasoners
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.
68d4a16436950a9dff15687e
Thaddäus Wiedemer
admin_assigned
2025-09-25T11:35:38.955Z
6635e88459d39b1afa736263
/avatars/0ebac4e2ba2420dfe7a852b873beb47a.svg
Thaddäus Wiedemer
user
ThaddaeusWiedemer
68d4a16436950a9dff15687f
Yuxuan Li
admin_assigned
2025-09-25T11:35:47.314Z
66d86ae34eb2eb8dc078f808
/avatars/4fb926729092ce69087343ce767387c8.svg
Yuxuan Li
user
yuxuanli
68d4a16436950a9dff156880
Paul Vicol
68d4a16436950a9dff156881
Shixiang Shane Gu
68d4a16436950a9dff156882
Nick Matarese
admin_assigned
2025-09-25T11:36:02.770Z
63e2b8f0c123238b189b9fed
/avatars/4f403225d37c758fe680a2f8dc0795e0.svg
Nick Matarese
user
nmatares
68d4a16436950a9dff156883
Kevin Swersky
admin_assigned
2025-09-25T11:36:10.350Z
630e6ef664f1f8d0c771b758
/avatars/1df2bfadb2b6fdf8307189936efc6ef0.svg
Kevin Swersky
user
kswersky
68d4a16436950a9dff156884
Been Kim
68d4a16436950a9dff156885
Priyank Jaini
68d4a16436950a9dff156886
Robert Geirhos
admin_assigned
2025-09-25T11:36:26.093Z
673bbe0d7dfcdedd52619ec2
/avatars/531a44f05d0c738bbe3e028c76c2e948.svg
Robert Geirhos
user
rgeirhos
Video models are zero-shot learners and reasoners
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.
Veo 3, a generative video model, exhibits zero-shot capabilities across various visual tasks, suggesting a trajectory towards becoming a unified, generalist vision foundation model.
Large Language Models LLMs generative models web-scale data generative video models zero-shot capabilities object segmentation edge detection image editing physical properties object affordances tool use simulation visual reasoning maze solving symmetry solving unified generalist vision foundation models