  Recent work has made significant progress on using implicit functions, as a
continuous representation for 3D rigid object shape reconstruction. However,
much less effort has been devoted to modeling general articulated objects.
Compared to rigid objects, articulated objects have higher degrees of freedom,
which makes it hard to generalize to unseen shapes. To deal with the large
shape variance, we introduce Articulated Signed Distance Functions (A-SDF) to
represent articulated shapes with a disentangled latent space, where we have
separate codes for encoding shape and articulation. We assume no prior
knowledge on part geometry, articulation status, joint type, joint axis, and
joint location. With this disentangled continuous representation, we
demonstrate that we can control the articulation input and animate unseen
instances with unseen joint angles. Furthermore, we propose a Test-Time
Adaptation inference algorithm to adjust our model during inference. We
demonstrate our model generalize well to out-of-distribution and unseen data,
e.g., partial point clouds and real-world depth images.
