Video Generation Models are General-Purpose Vision Learners
What, then, is the equivalent catalyst needed to achieve a general-purpose model in computer vision? In this paper, we contend that large-scale text-to-video generation serves as a strong pre-training paradigm for computer vision, providing the necessary spatiotemporal priors, vision-language alignment, and scalability required for general visual intelligence. We introduce GenCeption, which leverages a pre-trained video generative diffusion backbone to define a feed-forward perception model, capable of performing various vision tasks steered by text instructions.
- ▪What, then, is the equivalent catalyst needed to achieve a general-purpose model in computer vision?
- ▪In this paper, we contend that large-scale text-to-video generation serves as a strong pre-training paradigm for computer vision, providing the necessary spatiotemporal priors, vision-language alignment, and scalability required for general
- ▪We introduce GenCeption, which leverages a pre-trained video generative diffusion backbone to define a feed-forward perception model, capable of performing various vision tasks steered by text instructions.
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Computer Science > Computer Vision and Pattern Recognition arXiv:2607.09024 (cs) [Submitted on 10 Jul 2026] Title:Video Generation Models are General-Purpose Vision Learners Authors:Letian Wang, Chuhan Zhang, Rishabh Kabra, Jasper Uijlings, Steven Waslander, Andrew Zisserman, Joao Carreira, Kaiming He, Misha Andriluka, Eduard Gabriel Bazavan, Andrei Zanfir, Cristian Sminchisescu View a PDF of the paper titled Video Generation Models are General-Purpose Vision Learners, by Letian Wang and 10 other authors View PDF HTML (experimental) Abstract:Driven by next-token prediction, NLP shifted from task-specific models into powerful generalist foundation models.
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