V-DPM: 4D Video Reconstruction with Dynamic Point Maps
Powerful 3D representations such as DUSt3R invariant point maps, which encode 3D shape and camera parameters, have significantly advanced feed forward 3D reconstruction. While point maps assume static scenes, Dynamic Point Maps (DPMs) extend this concept to dynamic 3D content by additionally representing scene motion. However, existing DPMs are limited to image pairs and, like DUSt3R, require post processing via optimization when more than two views are involved. We argue that DPMs are more useful when applied to videos and introduce V-DPM to demonstrate this. First, we show how to formulate DPMs for video input in a way that maximizes representational power, facilitates neural prediction, and enables reuse of pretrained models. Second, we implement these ideas on top of VGGT, a recent and powerful 3D reconstructor. Although VGGT was trained on static scenes, we show that a modest amount of synthetic data is sufficient to adapt it into an effective V-DPM predictor. Our approach achieves state of the art performance in 3D and 4D reconstruction for dynamic scenes. In particular, unlike recent dynamic extensions of VGGT such as P3, DPMs recover not only dynamic depth but also the full 3D motion of every point in the scene.
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Edgar Sucar
admin_assigned
2026-01-16T15:40:19.827Z
6943e8596f46bd27c14dda56
/avatars/13b28d18550bbeea24a8d0a39282ecb9.svg
Edgar Sucar
edgarsucar
user
edgarsucar
6968a3720ac10a06522f6b6f
Eldar Insafutdinov
claimed_verified
2026-01-15T09:33:08.932Z
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Eldar Insafutdinov
einsafutdinov
user
einsafutdinov
6968a3720ac10a06522f6b70
Zihang Lai
admin_assigned
2026-01-16T15:40:25.207Z
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Zihang Lai
zlai
user
zlai
6968a3720ac10a06522f6b71
Andrea Vedaldi
admin_assigned
2026-01-16T15:40:30.543Z
68055d1082c2bae6af503848
/avatars/e32c2f0a8efbf54707ead520ae9374bf.svg
Andrea Vedaldi
vedaldi
user
vedaldi
V-DPM: 4D Video Reconstruction with Dynamic Point Maps
Powerful 3D representations such as DUSt3R invariant point maps, which encode 3D shape and camera parameters, have significantly advanced feed forward 3D reconstruction. While point maps assume static scenes, Dynamic Point Maps (DPMs) extend this concept to dynamic 3D content by additionally representing scene motion. However, existing DPMs are limited to image pairs and, like DUSt3R, require post processing via optimization when more than two views are involved. We argue that DPMs are more useful when applied to videos and introduce V-DPM to demonstrate this. First, we show how to formulate DPMs for video input in a way that maximizes representational power, facilitates neural prediction, and enables reuse of pretrained models. Second, we implement these ideas on top of VGGT, a recent and powerful 3D reconstructor. Although VGGT was trained on static scenes, we show that a modest amount of synthetic data is sufficient to adapt it into an effective V-DPM predictor. Our approach achieves state of the art performance in 3D and 4D reconstruction for dynamic scenes. In particular, unlike recent dynamic extensions of VGGT such as P3, DPMs recover not only dynamic depth but also the full 3D motion of every point in the scene.
Dynamic Point Maps extended to video input through V-DPM framework achieve state-of-the-art 3D and 4D reconstruction by recovering both dynamic depth and full 3D motion of scene points.
Dynamic Point Maps 3D reconstruction video input VGGT synthetic data 4D reconstruction 3D motion recovery