Video Depth without Video Models
Video depth estimation lifts monocular video clips to 3D by inferring dense
depth at every frame. Recent advances in single-image depth estimation, brought
about by the rise of large foundation models and the use of synthetic training
data, have fueled a renewed interest in video depth. However, naively applying
a single-image depth estimator to every frame of a video disregards temporal
continuity, which not only leads to flickering but may also break when camera
motion causes sudden changes in depth range. An obvious and principled solution
would be to build on top of video foundation models, but these come with their
own limitations; including expensive training and inference, imperfect 3D
consistency, and stitching routines for the fixed-length (short) outputs. We
take a step back and demonstrate how to turn a single-image latent diffusion
model (LDM) into a state-of-the-art video depth estimator. Our model, which we
call RollingDepth, has two main ingredients: (i) a multi-frame depth estimator
that is derived from a single-image LDM and maps very short video snippets
(typically frame triplets) to depth snippets. (ii) a robust, optimization-based
registration algorithm that optimally assembles depth snippets sampled at
various different frame rates back into a consistent video. RollingDepth is
able to efficiently handle long videos with hundreds of frames and delivers
more accurate depth videos than both dedicated video depth estimators and
high-performing single-frame models. Project page: rollingdepth.github.io.
674d64114e69219448bcf70d
Bingxin Ke
admin_assigned
2024-12-02T09:44:14.028Z
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Bingxin Ke
user
Bingxin
674d64114e69219448bcf70e
Dominik Narnhofer
admin_assigned
2024-12-02T09:44:18.859Z
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/avatars/41b60f776a41d85da9ea0b1ed7d7615b.svg
Dominik Narnhofer
user
dnarnhofer
674d64114e69219448bcf70f
Shengyu Huang
admin_assigned
2024-12-02T09:44:25.115Z
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Sheng-Yu Huang
user
peterjohnson
674d64114e69219448bcf710
Lei Ke
claimed_verified
2024-12-02T17:31:56.753Z
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Lei Ke
user
lkeab
674d64114e69219448bcf711
Torben Peters
admin_assigned
2024-12-02T09:44:39.755Z
656d886ad848a6683a0f71c3
/avatars/494f5fd5d5fa7849850cfe21f574f12c.svg
Torben Peters
user
PeterTor
674d64114e69219448bcf712
Katerina Fragkiadaki
674d64114e69219448bcf713
Anton Obukhov
admin_assigned
2024-12-02T09:44:55.130Z
62f93abbc4817cfc0756b6f8
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Anton Obukhov
user
toshas
674d64114e69219448bcf714
Konrad Schindler
claimed_verified
2025-05-15T13:45:32.605Z
6750649b7edd6a98a1bbcd06
/avatars/ba27f12d0333cf2d400d4405af7efe97.svg
Konrad Schindler
user
konradschindler
Video Depth without Video Models
Video depth estimation lifts monocular video clips to 3D by inferring dense
depth at every frame. Recent advances in single-image depth estimation, brought
about by the rise of large foundation models and the use of synthetic training
data, have fueled a renewed interest in video depth. However, naively applying
a single-image depth estimator to every frame of a video disregards temporal
continuity, which not only leads to flickering but may also break when camera
motion causes sudden changes in depth range. An obvious and principled solution
would be to build on top of video foundation models, but these come with their
own limitations; including expensive training and inference, imperfect 3D
consistency, and stitching routines for the fixed-length (short) outputs. We
take a step back and demonstrate how to turn a single-image latent diffusion
model (LDM) into a state-of-the-art video depth estimator. Our model, which we
call RollingDepth, has two main ingredients: (i) a multi-frame depth estimator
that is derived from a single-image LDM and maps very short video snippets
(typically frame triplets) to depth snippets. (ii) a robust, optimization-based
registration algorithm that optimally assembles depth snippets sampled at
various different frame rates back into a consistent video. RollingDepth is
able to efficiently handle long videos with hundreds of frames and delivers
more accurate depth videos than both dedicated video depth estimators and
high-performing single-frame models. Project page: rollingdepth.github.io.
RollingDepth uses a single-image latent diffusion model to estimate video depth, incorporating multi-frame estimation and optimization for high accuracy and efficiency.
latent diffusion model depth estimation video depth single-image multi-frame depth estimator optimization-based registration algorithm 3D consistency