Existing video depth estimation faces a fundamental trade-off: generative models suffer from stochastic geometric hallucinations and scale drift, while discriminative models demand massive labeled datasets to resolve semantic ambiguities. To break this impasse, we present DVD, the first framework to deterministically adapt pre-trained video diffusion models into single-pass depth regressors. Specifically, DVD features three core designs: (i) repurposing the diffusion timestep as a structural anchor to balance global stability with high-frequency details; (ii) latent manifold rectification (LMR) to mitigate regression-induced over-smoothing, enforcing differential constraints to restore sharp boundaries and coherent motion; and (iii) global affine coherence, an inherent property bounding inter-window divergence, which enables seamless long-video inference without requiring complex temporal alignment. Extensive experiments demonstrate that DVD achieves state-of-the-art zero-shot performance across benchmarks. Furthermore, DVD successfully unlocks the profound geometric priors implicit in video foundation models using 163x less task-specific data than leading baselines. Notably, we fully release our pipeline, providing the whole training suite for SOTA video depth estimation to benefit the open-source community.
@article{arxiv.2603.12250,
title = {DVD: Deterministic Video Depth Estimation with Generative Priors},
author = {Hongfei Zhang and Harold Haodong Chen and Chenfei Liao and Jing He and Zixin Zhang and Haodong Li and Yihao Liang and Kanghao Chen and Bin Ren and Xu Zheng and Shuai Yang and Kun Zhou and Yinchuan Li and Nicu Sebe and Ying-Cong Chen},
journal= {arXiv preprint arXiv:2603.12250},
year = {2026}
}