视频生成模型：视觉领域的GPT时刻？
我们总以为视频生成只是用来做视频的，但这篇论文告诉你：它可能是训练通用视觉AI的最佳方式。研究者用视频生成模型作为预训练骨干，然后直接用它来做深度估计、3D关键点预测、分割等任务，结果不仅超过了专门为这些任务设计的模型，而且训练数据只需后者的1/7到1/500。更意外的是，只用合成人类视频训练的模型，居然能直接看懂真实世界的动物和机器人。这不是你明天就能用的工具，但它暗示了一个方向：未来视觉AI可能不再需要为每个任务单独训练，而是像GPT一样，一个模型通吃所有。
📄 原文摘要(英文)
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 results demonstrate that GenCeption achieves state-of-the-art performance across a diverse suite of tasks, including depth, surface normal, and camera pose estimation, expression-referring segmentation, and 3D keypoint prediction, often matching or surpassing specialized models (e.g. DepthAnything3, SAM3, D4RT, VGGT-Omega, Sapiens, David, Genmo, and Lotus-2). Furthermore, the video generative pretrained backbone outperforms alternative pretraining paradigms (e.g., V-JEPA, and Video MAE) under comparable settings. Importantly, GenCeption exhibits preliminary data and model scaling properties along with exceptional data efficiency, where it achieves comparable performance with leading models like D4RT and VGGT-Omega with 7 to 500 less training data. Finally, GenCeption also exhibits intriguing emergent behaviors: a model trained exclusively on synthetic human videos generalizes to real-world footage and out-of-distribution object categories (e.g., animals and robots). These findings suggest that video generation is not merely a synthesis tool, but a foundational path toward generalist vision intelligence for the physical world. Project page: https://genception.github.io