视频生成模型：视觉领域的GPT时刻？
AI圈有个共识：语言模型靠“预测下一个词”成了通才，那视觉的通才靠什么？这篇论文给出一个反直觉的答案——视频生成。研究者把视频生成模型当成视觉预训练任务，然后直接用它做深度估计、3D关键点、分割等任务，结果不仅超过了很多专用模型，还展现出惊人的数据效率：只用1/500的训练数据就能达到顶级模型的效果。更意外的是，模型只在合成人类视频上训练，却能泛化到真实动物和机器人上。这不是一个你能明天用上的工具，但它暗示了一个方向：视频生成可能成为视觉领域的“GPT时刻”，让一个模型学会看世界的一切。
📄 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