Existing works on 2D pose estimation mainly focus on a certain category, e.g.
human, animal, and vehicle. However, there are lots of application scenarios
that require detecting the poses/keypoints of the unseen class of objects. In
this paper, we introduce the task of Category-Agnostic Pose Estimation (CAPE),
which aims to create a pose estimation model capable of detecting the pose of
any class of object given only a few samples with keypoint definition. To
achieve this goal, we formulate the pose estimation problem as a keypoint
matching problem and design a novel CAPE framework, termed POse Matching
Network (POMNet). A transformer-based Keypoint Interaction Module (KIM) is
proposed to capture both the interactions among different keypoints and the
relationship between the support and query images. We also introduce
Multi-category Pose (MP-100) dataset, which is a 2D pose dataset of 100 object
categories containing over 20K instances and is well-designed for developing
CAPE algorithms. Experiments show that our method outperforms other baseline
approaches by a large margin. Codes and data are available at
this https URL

本文提出了一种 CAPEG 框架，即 POSE Matching Network，并提出了一种基于 transformer 的 Keypoint Interaction Module，能够捕捉不同关键点之间的交互和支持和查询图像之间的关系，同时介绍了一个新的 MP-100 数据集，使用实验表明我们的方法比其他基线方法表现更好。