DVD: Deterministic Video Depth Estimation with Generative Priors
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.
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Hongfei Zhang
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2026-04-07T08:48:16.059Z
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Hongfei (Faye) Zhang
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Harold Haodong Chen
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2026-03-13T08:12:05.915Z
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Harold Chen
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Chenfei Liao
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2026-03-26T14:04:30.316Z
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Chenfei Liao
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Jing He
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Zixin Zhang
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2026-03-16T10:43:06.867Z
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Zixin Zhang
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Haodong Li
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2026-03-13T08:12:08.858Z
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Haodong Li
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Yihao Liang
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Kanghao Chen
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Bin Ren
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Xu Zheng
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Shuai Yang
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Kun Zhou
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Yinchuan Li
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Nicu Sebe
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Ying-Cong Chen
DVD: Deterministic Video Depth Estimation with Generative Priors
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.
Video depth estimation framework DVD adapts pre-trained video diffusion models into deterministic single-pass depth regressors using structural anchors, latent manifold rectification, and global affine coherence for improved accuracy and efficiency.
video diffusion models depth regressors diffusion timestep latent manifold rectification global affine coherence video foundation models zero-shot performance task-specific data