Consistent Depth of Moving Objects in Video

Zhoutong Zhang 1, 2 Forrester Cole 1 Richard Tucker 1 William T. Freeman 1,2 Tali Dekel 1, 3

 1 Google Research  2 MIT  3 Weizmann Institute of Science

Our method estimates geometrically and temporally consistent depth from a general video containing fast-moving objects and camera motion. The input video (a) contains a continuous camera dolly motion following the moving person and puppy. This is a difficult case for depth estimation due to the correlated motion between camera and subject. The video is shown reprojected into the camera at frame t using our predicted depth (b), with disparity maps of the re-projection shown below (c). On the bottom: x-t slices for the horizontal line marked in red on frame t in (a). (d) The slice of the original video (top) shows both camera and objects' motion (slanted lines in the background, twisted lines in the foreground). The slices of the re-projected frames(e)(f) show the camera fixed relative to the background (vertical lines), and foreground objects moving relative to the camera (twisted lines).


We present a method to estimate depth of a dynamic scene, containing arbitrary moving objects, from an ordinary video captured with a moving camera. We seek a geometrically and temporally consistent solution to this underconstrained problem: the depth predictions of corresponding points across frames should induce plausible, smooth motion in 3D. We formulate this objective in a new test-time training framework where a depth-prediction CNN is trained in tandem with an auxiliary scene-flow prediction MLP over the entire input video. By recursively unrolling the scene-flow prediction MLP over varying time steps, we compute both short-range scene flow to impose local smooth motion priors directly in 3D, and long-range scene flow to impose multi-view consistency constraints with wide baselines. We demonstrate accurate and temporally coherent results on a variety of challenging videos containing diverse moving objects (pets, people, cars), as well as camera motion. Our depth maps give rise to a number of depth-and-motion aware video editing effects such as object and lighting insertion.



5 min talk:

15-minute version is available on YouTube.




Consistent Depth of Moving Objects in Video
Zhoutong Zhang, Forrester Cole, Richard Tucker, William T. Freeman, Tali Dekel



Supplementary Material

[supplementary page]






    title={Consistent depth of moving objects in video},
    author={Zhang, Zhoutong and Cole, Forrester and Tucker, Richard and Freeman, William T
            and Dekel, Tali},
    journal={ACM Transactions on Graphics (TOG)},
    publisher={ACM New York, NY, USA}


Related Works

Consistent Video Depth Estimation
Xuan Luo, Jia-Bin Huang, Richard Szeliski, Kevin Matzen, Johannes Kopf. SIGGRAPH 2020.

Learning the Depths of Moving People by Watching Frozen People
Zhengqi Li, Tali Dekel, Forrester Cole, Richard Tucker, Noah Snavely, Ce Liu, William T. Freeman. CVPR 2019