Our self-supervised method learns the shape and appearance of articulated object classes. After training from multi-view synthetic images of different states of object instances, our model can reconstruct and animate objects from static real-world images
Learning geometry, motion, and appearance priors of object classes is important for the solution of a large variety of computer vision problems. While the majority of approaches has focused on static objects, dynamic objects, especially with controllable articulation, are less explored. We propose a novel approach for learning a representation of the geometry, appearance, and motion of a class of articulated objects given only a set of color images as input. In a self-supervised manner, our novel representation learns shape, appearance, and articulation codes that enable independent control of these semantic dimensions. Our model is trained end-to-end without requiring any articulation annotations. Experiments show that our approach performs well for different joint types, such as revolute and prismatic joints, as well as different combinations of these joints. Compared to state of the art that uses direct 3D supervision and does not output appearance, we recover more faithful geometry and appearance from 2D observations only. In addition, our representation enables a large variety of applications, such as few-shot reconstruction, the generation of novel articulations, and novel view-synthesis.
Input Static RGB Images
Output Animation
Input Static RGB Images
Output Animation
While A-SDF loses more details and the shape of non-motion parts may also change in A-SDF, our method predicts static non-motion parts and more accurate geometry during interpolation and extrapolation.
