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We build rearticulable models for arbitrary everyday man-made objectscontaining an arbitrary number of parts that are connected together inarbitrary ways via 1 degree-of-freedom joints. Given point cloud videos of sucheveryday objects, our method identifies the distinct object parts, what partsare connected to what other parts, and the properties of the joints connectingeach part pair. We do this by jointly optimizing the part segmentation,transformation, and kinematics using a novel energy minimization framework. Ourinferred animatable models, enables retargeting to novel poses with sparsepoint correspondences guidance. We test our method on a new articulating robotdataset, and the Sapiens dataset with common daily objects, as well asreal-world scans. Experiments show that our method outperforms two leadingprior works on various metrics.

Building Rearticulable Models for Arbitrary 3D Objects from 4D Point Clouds