  Traditional 2D pose estimation models are limited by their category-specific
design, making them suitable only for predefined object categories. This
restriction becomes particularly challenging when dealing with novel objects
due to the lack of relevant training data. To address this limitation,
category-agnostic pose estimation (CAPE) was introduced. CAPE aims to enable
keypoint localization for arbitrary object categories using a few-shot single
model, requiring minimal support images with annotated keypoints. We present a
significant departure from conventional CAPE techniques, which treat keypoints
as isolated entities, by treating the input pose data as a graph. We leverage
the inherent geometrical relations between keypoints through a graph-based
network to break symmetry, preserve structure, and better handle occlusions. We
validate our approach on the MP-100 benchmark, a comprehensive dataset
comprising over 20,000 images spanning over 100 categories. Our solution boosts
performance by 0.98% under a 1-shot setting, achieving a new state-of-the-art
for CAPE. Additionally, we enhance the dataset with skeleton annotations. Our
code and data are publicly available.
