PAD3R: Pose-Aware Dynamic 3D Reconstruction from Casual Videos

• URL: http://arxiv.org/abs/2509.25183v1
• Date: Mon, 29 Sep 2025 17:59:33 GMT
• Title: PAD3R: Pose-Aware Dynamic 3D Reconstruction from Casual Videos
• Authors: Ting-Hsuan Liao, Haowen Liu, Yiran Xu, Songwei Ge, Gengshan Yang, Jia-Bin Huang,
• Abstract summary: PAD3R is a method for reconstructing deformable 3D objects from casually captured, unposed monocular videos.<n>At its core, our approach trains a personalized, object-centric pose estimator, supervised by a pre-trained image-to-3D model.<n>By combining generative priors and differentiable rendering, PAD3R reconstructs high-fidelity, articulated 3D representations of objects in a category-agnostic way.
• Score: 25.79551555341372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present PAD3R, a method for reconstructing deformable 3D objects from casually captured, unposed monocular videos. Unlike existing approaches, PAD3R handles long video sequences featuring substantial object deformation, large-scale camera movement, and limited view coverage that typically challenge conventional systems. At its core, our approach trains a personalized, object-centric pose estimator, supervised by a pre-trained image-to-3D model. This guides the optimization of deformable 3D Gaussian representation. The optimization is further regularized by long-term 2D point tracking over the entire input video. By combining generative priors and differentiable rendering, PAD3R reconstructs high-fidelity, articulated 3D representations of objects in a category-agnostic way. Extensive qualitative and quantitative results show that PAD3R is robust and generalizes well across challenging scenarios, highlighting its potential for dynamic scene understanding and 3D content creation.

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