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arXiv · 2509.25183

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

Ting-Hsuan Liao·Haowen Liu·Yiran Xu·Songwei Ge·Gengshan Yang·Jia-Bin Huang
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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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Ting-Hsuan Liao, Haowen Liu, Yiran Xu, Songwei Ge, Gengshan Yang, Jia-Bin Huang. 2025-09-29. PAD3R: Pose-Aware Dynamic 3D Reconstruction from Casual Videos. https://arxiv.org/abs/2509.25183

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