超越像素历史：具有持久3D状态的世界模型
Beyond Pixel Histories: World Models with Persistent 3D State
AI总结：
提出PERSIST范式，通过模拟潜在3D场景（环境、相机、渲染器）的演化，实现具有持久空间记忆和一致几何的世界模型，显著提升3D一致性、空间记忆和长期稳定性。
摘要：
交互式世界模型通过响应用户的动作持续生成视频，实现开放式的生成能力。然而，现有模型通常缺乏环境的3D表示，意味着3D一致性必须从数据中隐式学习，且空间记忆受限于有限的时域上下文窗口。这导致不真实的用户体验，并对训练智能体等下游任务构成重大障碍。为解决这一问题，我们提出PERSIST，一种新的世界模型范式，它模拟潜在3D场景（环境、相机和渲染器）的演化。这使得我们能够合成具有持久空间记忆和一致几何的新帧。定量指标和定性用户研究均表明，与现有方法相比，在空间记忆、3D一致性和长期稳定性方面有显著提升，从而实现连贯、演化的3D世界。我们进一步展示了新颖的能力，包括从单张图像合成多样化的3D环境，以及通过直接在3D空间中支持环境编辑和指定，实现对生成体验的细粒度、几何感知控制。项目页面：https://francelico.github.io/persist.github.io
英文摘要：
Interactive world models continually generate video by responding to a user's actions, enabling open-ended generation capabilities. However, existing models typically lack a 3D representation of the environment, meaning 3D consistency must be implicitly learned from data, and spatial memory is restricted to limited temporal context windows. This results in an unrealistic user experience and presents significant obstacles to downstream tasks such as training agents. To address this, we present PERSIST, a new paradigm of world model which simulates the evolution of a latent 3D scene: environment, camera, and renderer. This allows us to synthesise new frames with persistent spatial memory and consistent geometry. Both quantitative metrics and a qualitative user study show substantial improvements in spatial memory, 3D consistency, and long-horizon stability over existing methods, enabling coherent, evolving 3D worlds. We further demonstrate novel capabilities, including synthesising diverse 3D environments from a single image, as well as enabling fine-grained, geometry-aware control over generated experiences by supporting environment editing and specification directly in 3D space. Project page: https://francelico.github.io/persist.github.io
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。
