Despite remarkable advancements in video depth estimation, existing methods exhibit inherent limitations in achieving geometric fidelity through the affine-invariant predictions, limiting their applicability in reconstruction and other metrically grounded downstream tasks. We propose GeometryCrafter, a novel framework that recovers high-fidelity point map sequences with temporal coherence from open-world videos, enabling accurate 3D/4D reconstruction, camera parameter estimation, and other depth-based applications. At the core of our approach lies a point map Variational Autoencoder (VAE) that learns a latent space agnostic to video latent distributions for effective point map encoding and decoding. Leveraging the VAE, we train a video diffusion model to model the distribution of point map sequences conditioned on the input videos. Extensive evaluations on diverse datasets demonstrate that GeometryCrafter achieves state-of-the-art 3D accuracy, temporal consistency, and generalization capability.
@article{arxiv.2504.01016,
title = {GeometryCrafter: Consistent Geometry Estimation for Open-world Videos with Diffusion Priors},
author = {Tian-Xing Xu and Xiangjun Gao and Wenbo Hu and Xiaoyu Li and Song-Hai Zhang and Ying Shan},
journal= {arXiv preprint arXiv:2504.01016},
year = {2025}
}