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arXiv · 2504.07961

Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction

Zeren Jiang·Chuanxia Zheng·Iro Laina·Diane Larlus·Andrea Vedaldi
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Abstract

We introduce Geo4D, a method to repurpose video diffusion models for monocular 3D reconstruction of dynamic scenes. By leveraging the strong dynamic priors captured by large-scale pre-trained video models, Geo4D can be trained using only synthetic data while generalizing well to real data in a zero-shot manner. Geo4D predicts several complementary geometric modalities, namely point, disparity, and ray maps. We propose a new multi-modal alignment algorithm to align and fuse these modalities, as well as a sliding window approach at inference time, thus enabling robust and accurate 4D reconstruction of long videos. Extensive experiments across multiple benchmarks show that Geo4D significantly surpasses state-of-the-art video depth estimation methods.

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Zeren Jiang, Chuanxia Zheng, Iro Laina, Diane Larlus, Andrea Vedaldi. 2025-04-10. Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction. https://arxiv.org/abs/2504.07961

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