  This paper tackles the task of category-level pose estimation for garments.
With a near infinite degree of freedom, a garment's full configuration (i.e.,
poses) is often described by the per-vertex 3D locations of its entire 3D
surface. However, garments are also commonly subject to extreme cases of
self-occlusion, especially when folded or crumpled, making it challenging to
perceive their full 3D surface. To address these challenges, we propose
GarmentNets, where the key idea is to formulate the deformable object pose
estimation problem as a shape completion task in the canonical space. This
canonical space is defined across garments instances within a category,
therefore, specifies the shared category-level pose. By mapping the observed
partial surface to the canonical space and completing it in this space, the
output representation describes the garment's full configuration using a
complete 3D mesh with the per-vertex canonical coordinate label. To properly
handle the thin 3D structure presented on garments, we proposed a novel 3D
shape representation using the generalized winding number field. Experiments
demonstrate that GarmentNets is able to generalize to unseen garment instances
and achieve significantly better performance compared to alternative
approaches.
