用人工智能模拟视觉世界：一条路线图
Simulating the Visual World with Artificial Intelligence: A Roadmap
AI总结：
本文提出了一种基于隐式世界模型和视频渲染器的视频基础模型，用于模拟物理动态、智能体交互和任务规划，涵盖四代视频生成技术，应用于机器人、自动驾驶和互动游戏等领域。
摘要：
视频生成的领域正在发生变化，从专注于生成视觉吸引人的片段，转向构建支持交互和保持物理合理性的虚拟环境。这些发展表明，视频基础模型的出现，这些模型不仅作为视觉生成器，还作为隐式世界模型，模拟真实或想象世界中的物理动态、智能体与环境的交互以及任务规划。本文系统地概述了这一演变，将现代视频基础模型概念化为两个核心组成部分的结合：隐式世界模型和视频渲染器。世界模型编码关于世界的结构化知识，包括物理定律、交互动态和智能体行为。它作为潜在的模拟引擎，使视觉推理、长期时间一致性和目标驱动的规划成为可能。视频渲染器将这一潜在模拟转化为逼真的视觉观察，有效地生成视频作为“窗口”进入模拟世界。我们通过四代视频生成的演进，逐步推进核心能力，最终以基于视频生成模型构建的世界模型为终点，该模型体现了内在的物理合理性、实时多模态交互以及跨多时空尺度的任务规划能力。对于每一代，我们定义其核心特征，突出代表性作品，并检查其应用领域，如机器人、自动驾驶和互动游戏。最后，我们讨论下一代世界模型的开放挑战和设计原则，包括智能体智能在塑造和评估这些系统中的作用。相关工作的最新列表可通过此链接维护。
英文摘要：
The landscape of video generation is shifting, from a focus on generating visually appealing clips to building virtual environments that support interaction and maintain physical plausibility. These developments point toward the emergence of video foundation models that function not only as visual generators but also as implicit world models, models that simulate the physical dynamics, agent-environment interactions, and task planning that govern real or imagined worlds. This survey provides a systematic overview of this evolution, conceptualizing modern video foundation models as the combination of two core components: an implicit world model and a video renderer. The world model encodes structured knowledge about the world, including physical laws, interaction dynamics, and agent behavior. It serves as a latent simulation engine that enables coherent visual reasoning, long-term temporal consistency, and goal-driven planning. The video renderer transforms this latent simulation into realistic visual observations, effectively producing videos as a "window" into the simulated world. We trace the progression of video generation through four generations, in which the core capabilities advance step by step, ultimately culminating in a world model, built upon a video generation model, that embodies intrinsic physical plausibility, real-time multimodal interaction, and planning capabilities spanning multiple spatiotemporal scales. For each generation, we define its core characteristics, highlight representative works, and examine their application domains such as robotics, autonomous driving, and interactive gaming. Finally, we discuss open challenges and design principles for next-generation world models, including the role of agent intelligence in shaping and evaluating these systems. An up-to-date list of related works is maintained at this link.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
