Video Generation Models are General-Purpose Vision Learners
Driven by next-token prediction, NLP shifted from task-specific models into powerful generalist foundation models. 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. Empirical re...
| Authors | Letian Wang, Chuhan Zhang, Rishabh Kabra et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 July 2026 |
| Last updated at source | 10 July 2026 |
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