Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction
Authors: name: Zeren Jiang, name: Chuanxia Zheng, name: Iro Laina, name: Diane Larlus, name: Andrea Vedaldi

We introduce Geo4D, a method to repurpose video diffusion models for
monocular 3D reconstruction of dynamic scenes. By leveraging the strong dynamic
prior captured by such 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, depth, and ray maps.
It uses a new multi-modal alignment algorithm to align and fuse these
modalities, as well as multiple sliding windows, at inference time, thus
obtaining 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, including recent methods such
as MonST3R, which are also designed to handle dynamic scenes.