Presentation
PAD3R: Pose-Aware Dynamic 3D Reconstruction from Casual Videos
DescriptionWe present a method for dynamic 3D reconstruction of deformable objects from casually captured, unposed monocular videos. Unlike existing approaches, our method handles long video sequences featuring substantial object deformation, large-scale camera movement, and limited view coverage that typically challenge conventional systems. Specifically, our approach first trains a personalized, object-centric pose estimation model utilizing a pre-trained image-to-3D diffusion model. This guides the optimization of a deformable 3D Gaussian representation and a neural skinning model, enhanced by a long-term point tracking regularization over the entire input video. By combining diffusion priors and differentiable rendering, our method reconstructs high-fidelity, articulated 3D representations of category-agnostic objects. Extensive qualitative and quantitative results show that our approach is robust and generalizes well across challenging scenarios, highlighting its potential for dynamic scene understanding and 3D content creation.
Event Type
Technical Papers
TimeWednesday, 17 December 20251:10pm - 1:20pm HKT
LocationMeeting Room S423+S424, Level 4