ChronosObserver: Taming 4D World with Hyperspace Diffusion Sampling

Qisen Wang, Yifan Zhao, Peisen Shen, Jialu Li, Jia Li

ChronosObserver: Taming 4D World with Hyperspace Diffusion Sampling: 3 upvotes on Hugging Face Daily Papers, #36 of 47 papers on 2025-12-02. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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