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Articulated objects (e.g., doors and drawers) exist everywhere in our life.Different from rigid objects, articulated objects have higher degrees offreedom and are rich in geometries, semantics, and part functions. Modelingdifferent kinds of parts and articulations with nerual networks plays anessential role in articulated object understanding and manipulation, and willfurther benefit 3D vision and robotics communities. To model articulatedobjects, most previous works directly encode articulated objects into featurerepresentations, without specific designs for parts, articulations and partmotions. In this paper, we introduce a novel framework that explicitlydisentangles the part motion of articulated objects by predicting thetransformation matrix of points on the part surface, using spatially continuousneural implicit representations to model the part motion smoothly in the space.More importantly, while many methods could only model a certain kind of jointmotion (such as the revolution in the clockwise order), our proposed frameworkis generic to different kinds of joint motions in that transformation matrixcan model diverse kinds of joint motions in the space. Quantitative andqualitative results of experiments over diverse categories of articulatedobjects demonstrate the effectiveness of our proposed framework.

Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations