  This work addresses the challenge of video depth estimation, which expects
not only per-frame accuracy but, more importantly, cross-frame consistency.
Instead of directly developing a depth estimator from scratch, we reformulate
the prediction task into a conditional generation problem. This allows us to
leverage the prior knowledge embedded in existing video generation models,
thereby reducing learning difficulty and enhancing generalizability.
Concretely, we study how to tame the public Stable Video Diffusion (SVD) to
predict reliable depth from input videos using a mixture of image depth and
video depth datasets. We empirically confirm that a procedural training
strategy -- first optimizing the spatial layers of SVD and then optimizing the
temporal layers while keeping the spatial layers frozen -- yields the best
results in terms of both spatial accuracy and temporal consistency. We further
examine the sliding window strategy for inference on arbitrarily long videos.
Our observations indicate a trade-off between efficiency and performance, with
a one-frame overlap already producing favorable results. Extensive experimental
results demonstrate the superiority of our approach, termed ChronoDepth, over
existing alternatives, particularly in terms of the temporal consistency of the
estimated depth. Additionally, we highlight the benefits of more consistent
video depth in two practical applications: depth-conditioned video generation
and novel view synthesis. Our project page is available at
https://jhaoshao.github.io/ChronoDepth/.
