Self-supervised Neural Articulated Shape and Appearance Models
This addresses the challenge of modeling dynamic, articulated objects in computer vision, enabling applications like few-shot reconstruction and novel view-synthesis, though it is incremental in extending self-supervised learning to articulated settings.
The paper tackles the problem of learning geometry, appearance, and motion priors for articulated objects from only 2D color images, without articulation annotations, and achieves more faithful geometry and appearance recovery compared to state-of-the-art methods that use 3D supervision.
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.