ChronosObserver：利用超空间扩散采样驯服四维世界 / ChronosObserver: Taming 4D World with Hyperspace Diffusion Sampling
1️⃣ 一句话总结
这篇论文提出了一种名为ChronosObserver的无训练方法，通过构建一个‘世界状态超空间’来表征四维场景的时空约束，并利用该超空间同步多个视角的扩散采样轨迹，从而直接生成高保真、三维一致且时间同步的多视角视频，无需对现有扩散模型进行额外训练或微调。
🤖 系统
Abstract - ChronosObserver: Taming 4D World with Hyperspace Diffusion Sampling
Although prevailing camera-controlled video generation models can produce cinematic results, lifting them directly to the generation of 3D-consistent and high-fidelity time-synchronized multi-view videos remains challenging, which is a pivotal capability for taming 4D worlds. Some works resort to data augmentation or test-time optimization, but these strategies are constrained by limited model generalization and scalability issues. To this end, we propose ChronosObserver, a training-free method including World State Hyperspace to represent the spatiotemporal constraints of a 4D world scene, and Hyperspace Guided Sampling to synchronize the diffusion sampling trajectories of multiple views using the hyperspace. Experimental results demonstrate that our method achieves high-fidelity and 3D-consistent time-synchronized multi-view videos generation without training or fine-tuning for diffusion models.