  We build rearticulable models for arbitrary everyday man-made objects
containing an arbitrary number of parts that are connected together in
arbitrary ways via 1 degree-of-freedom joints. Given point cloud videos of such
everyday objects, our method identifies the distinct object parts, what parts
are connected to what other parts, and the properties of the joints connecting
each part pair. We do this by jointly optimizing the part segmentation,
transformation, and kinematics using a novel energy minimization framework. Our
inferred animatable models, enables retargeting to novel poses with sparse
point correspondences guidance. We test our method on a new articulating robot
dataset, and the Sapiens dataset with common daily objects, as well as
real-world scans. Experiments show that our method outperforms two leading
prior works on various metrics.
