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We propose CLA-NeRF -- a Category-Level Articulated Neural Radiance Fieldthat can perform view synthesis, part segmentation, and articulated poseestimation. CLA-NeRF is trained at the object category level using no CADmodels and no depth, but a set of RGB images with ground truth camera poses andpart segments. During inference, it only takes a few RGB views (i.e., few-shot)of an unseen 3D object instance within the known category to infer the objectpart segmentation and the neural radiance field. Given an articulated pose asinput, CLA-NeRF can perform articulation-aware volume rendering to generate thecorresponding RGB image at any camera pose. Moreover, the articulated pose ofan object can be estimated via inverse rendering. In our experiments, weevaluate the framework across five categories on both synthetic and real-worlddata. In all cases, our method shows realistic deformation results and accuratearticulated pose estimation. We believe that both few-shot articulated objectrendering and articulated pose estimation open doors for robots to perceive andinteract with unseen articulated objects.

CLA-NeRF: Category-Level Articulated Neural Radiance Field