{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pose-for-everything-towards-category-agnostic","title":"Pose for Everything: Towards Category-Agnostic Pose Estimation","arxiv_id":"2207.10387","date":"2022-07-21","proceeding":null,"authors":["Lumin Xu","Sheng Jin","Wang Zeng","Wentao Liu","Chen Qian","Wanli Ouyang","Ping Luo","Xiaogang Wang"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2207.10387v1","url_pdf":"https://arxiv.org/pdf/2207.10387v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pose-for-everything-towards-category-agnostic","repo_url":"https://github.com/luminxu/pose-for-everything","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"2d-pose-estimation","task_name":"2D Pose Estimation"},{"task_slug":"category-agnostic-pose-estimation","task_name":"Category-Agnostic Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[{"slug":"mp-100","name":"MP-100","full_name":"Mulit-category Pose Dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/2d-pose-estimation-on-mp-100","task":"2D Pose Estimation","dataset":"MP-100","model":"POMNet","rank_in_archive_order":6,"of":6,"metrics":{"Mean PCK@0.2 - 1shot":"79.70","Mean PCK@0.2 - 5shot":"80.71"},"uses_additional_data":false},{"leaderboard":"/sota/category-agnostic-pose-estimation-on-mp100","task":"Category-Agnostic Pose Estimation","dataset":"MP100","model":"POMNet","rank_in_archive_order":2,"of":5,"metrics":{"Mean PCK@0.2 - 1shot":"79.70"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.10387","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10387"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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