Pose for Everything: Towards Category-Agnostic Pose Estimation

• Published in: Computer Vision - ECCV 2022 Vol. 13666; pp. 398 - 416
• Main Authors: Xu, Lumin, Jin, Sheng, Zeng, Wang, Liu, Wentao, Qian, Chen, Ouyang, Wanli, Luo, Ping, Wang, Xiaogang
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer 2022; Springer Nature Switzerland
• Series: Lecture Notes in Computer Science
• Subjects: 2D pose estimation; Class-agnostic; Few-shot; MP-100 dataset
• ISBN: 9783031200670; 3031200675
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-031-20068-7_23

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 https://github.com/luminxu/Pose-for-Everything.