myWiki/raw/papers/genception-video-vision-2026.md

Video Generation Models are General-Purpose Vision Learners (GenCeption)

• arXiv: 2607.09024v1
• Published: 2026-07-10
• Venue: ECCV 2026
• Categories: cs.CV, cs.AI
• 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
• Affiliations: Google DeepMind, University of Toronto, UCL, University of Oxford, MIT, Lund University
• Project: https://genception.github.io

Abstract

The paper argues that large-scale text-to-video generation serves as a strong pre-training paradigm for computer vision — the visual equivalent of next-token prediction in NLP. GenCeption repurposes a pre-trained video generative diffusion backbone into a feed-forward perception model capable of diverse vision tasks steered by text instructions. It achieves SOTA across depth, surface normal, camera pose estimation, expression-referring segmentation, and 3D keypoint prediction, often matching or surpassing specialized models with 7x-500x less training data. Notably, a model trained exclusively on synthetic human videos generalizes to real-world footage and OOD categories (animals, robots).

Key Contributions

1. Video Generation as Universal Vision Pre-training: satisfies three imperatives—spatio-temporal priors, vision-language alignment, and scalability
2. Feed-Forward Repurposing: iterative diffusion → single-step perception via conditioning on t=0 and negating velocity output
3. Unified Task Representation: all dense tasks in 3-channel RGB space; sparse tasks via learnable tokens
4. Data Efficiency: matches SOTA specialists with 7x-500x less training data
5. Emergent Sim-to-Real Transfer: synthetic-only training generalizes to real-world and OOD categories