ChronosObserver: Taming 4D World with Hyperspace Diffusion Sampling

TL;DR

ChronosObserver is a training-free approach that uses hyperspace diffusion sampling to generate high-fidelity, 3D-consistent multi-view videos of 4D worlds, overcoming limitations of previous methods.

Contribution

It introduces a novel training-free framework with hyperspace representation and guided sampling for synchronized multi-view video generation.

Findings

Achieves high-fidelity, 3D-consistent multi-view videos.

Does not require training or fine-tuning of diffusion models.

Demonstrates scalability and generalization in 4D scene synthesis.

Abstract

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.