Abstract
We introduce PerpetualWonder, a hybrid generative simulator that enables long-horizon, action-conditioned 4D scene generation from a single image. Current works fail at this task because their physical state is decoupled from their visual representation, which prevents generative refinements to update the underlying physics for subsequent interactions. PerpetualWonder solves this by introducing the first true closed-loop system. It features a novel unified representation that creates a bidirectional link between the physical state and visual primitives, allowing generative refinements to correct both the dynamics and appearance. It also introduces a robust update mechanism that gathers supervision from multiple viewpoints to resolve optimization ambiguity. Experiments demonstrate that from a single image, PerpetualWonder can successfully simulate complex, multi-step interactions from long-horizon actions, maintaining physical plausibility and visual consistency.
Abstract (translated)
我们介绍了PerpetualWonder，这是一个混合生成模拟器，它能够从单张图像中生成长期时序、动作条件下的4D场景。当前的工作在此任务上表现不佳，因为它们的物理状态与其视觉表示是脱钩的，这阻碍了生成性细化以更新后续交互的基础物理原理。PerpetualWonder通过引入首个真正的闭环系统解决了这一问题。它具备一种新颖的统一表征方法，这种方法在物理状态和视觉元素之间创建了一个双向联系，使得生成性的细化可以同时修正动态和外观。此外，它还推出了一种稳健的更新机制，该机制可以从多个视角收集监督信息以解决优化模糊性。 实验表明，从单张图像出发，PerpetualWonder能够成功模拟复杂的多步交互，并且这些交互来自长期动作序列，同时还保持物理真实性和视觉一致性。
URL
https://arxiv.org/abs/2602.04876
