DVD: Deterministic Video Depth Estimation with Generative Priors

Existing video depth estimation faces a fundamental trade-off: generative models often suffer from geometric hallucinations (e.g., inconsistent geometry structures) and scale drift, while discriminative models demand massive labeled datasets to resolve semantic ambiguities (e.g., misinterpreting textures or semantic boundaries as geometric structures). To mitigate this trade-off, we present DVD, to the best of our knowledge, the first framework to deterministically adapt pre-trained video diffusion models into single-pass depth regressors. DVD introduces three targeted adaptation strategies to effectively ground generative priors: (i) fixing the diffusion timestep as a structural anchor to optimally balance global stability with high-frequency details; (ii) applying latent manifold rectification (LMR), a simple parameter-free strategy to counteract regression-induced mean collapse, a critical issue that erases geometry information; and (iii) uncovering and leveraging the model's inherent global affine coherence for a straightforward, overlap-based alignment strategy, enabling seamless long-video inference. Extensive experiments demonstrate that [OurMethod] achieves state-of-the-art zero-shot performance across standard video benchmarks. Crucially, by inheriting the profound world priors of video foundation models, this deterministic adaptation paradigm proves highly data-efficient, requiring only 367K task-specific downstream training frames to adapt. We will fully release our pipeline to benefit the open-source community.