  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 single model, requiring minimal support images with
annotated keypoints. This approach not only enables object pose generation
based on arbitrary keypoint definitions but also significantly reduces the
associated costs, paving the way for versatile and adaptable pose estimation
applications.
  We present a novel approach to CAPE that leverages the inherent geometrical
relations between keypoints through a newly designed Graph Transformer Decoder.
By capturing and incorporating this crucial structural information, our method
enhances the accuracy of keypoint localization, marking a significant departure
from conventional CAPE techniques that treat keypoints as isolated entities.
  We validate our approach on the MP-100 benchmark, a comprehensive dataset
comprising over 20,000 images spanning more than 100 categories. Our method
outperforms the prior state-of-the-art by substantial margins, achieving
remarkable improvements of 2.16% and 1.82% under 1-shot and 5-shot settings,
respectively. Furthermore, our method's end-to-end training demonstrates both
scalability and efficiency compared to previous CAPE approaches.

Computer Vision and Pattern Recognition