V-DPM: Video reconstruction with Dynamic Point Maps

Conference item

Powerful 3D representations such as DUSt3R’s 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 the concept to dynamic 3D content by also representing scene motion. However, DPMs have so far been limited to image pairs and, like DUSt3R, require post-processing via optimisation 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 set up DPMs for videos to optimise representational power, facilitate neural prediction, and enable reuse of pretrained models. Second, we implement these ideas on top of VGGT, a recent powerful 3D reconstructor. Although VGGT was trained on static scenes, we show that a modest amount of synthetic data suffices to adapt it into an effective V-DPM predictor. This yields state-of-the-art 3D and 4D reconstruction in dynamic settings. In particular, unlike recent dynamic extensions of VGGT such as P3, DPMs recover not only dynamic depth but also the 3D motion of every point in the scene.

Authors: Sucar, E; Insafutdinov, E; Lai, Z; Vedaldi, A

• Publication status: Accepted
• Peer review status: Peer reviewed
• Publisher: IEEE
• Acceptance date: 2026-04-18
• Event title: IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026
• Event location: Denver, CO, USA
• Event website: https://cvpr.thecvf.com/Conferences/2026
• Event start date: 2026-06-03
• Event end date: 2026-06-07
• Language: English