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.

翻译：我们为任意日常人造物体构建可重构模型，这些物体包含任意数量的部件，并通过单自由度关节以任意方式相互连接。给定此类日常物体的点云视频，我们的方法能够识别不同的部件、部件之间的连接关系，以及连接每个部件对的关节属性。我们通过一种新颖的能量最小化框架，联合优化部件分割、变换和运动学来实现这一目标。我们推断出的可动画模型，能够在稀疏点对应引导下重定向至新姿态。我们在一个新的人形机器人数据集、包含常见日常物体的Sapiens数据集以及真实场景扫描上测试了该方法。实验表明，我们的方法在多种指标上均优于两项领先的先前工作。