{"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/capellm-support-free-category-agnostic-pose","title":"CapeLLM: Support-Free Category-Agnostic Pose Estimation with Multimodal Large Language Models","arxiv_id":"2411.06869","date":"2024-11-11","proceeding":null,"authors":["Junho Kim","Hyungjin Chung","Byung-Hoon Kim"],"abstract":"Category-agnostic pose estimation (CAPE) has traditionally relied on support images with annotated keypoints, a process that is often cumbersome and may fail to fully capture the necessary correspondences across diverse object categories. Recent efforts have begun exploring the use of text-based queries, where the need for support keypoints is eliminated. However, the optimal use of textual descriptions for keypoints remains an underexplored area. In this work, we introduce CapeLLM, a novel approach that leverages a text-based multimodal large language model (MLLM) for CAPE. Our method only employs query image and detailed text descriptions as an input to estimate category-agnostic keypoints. We conduct extensive experiments to systematically explore the design space of LLM-based CAPE, investigating factors such as choosing the optimal description for keypoints, neural network architectures, and training strategies. Thanks to the advanced reasoning capabilities of the pre-trained MLLM, CapeLLM demonstrates superior generalization and robust performance. Our approach sets a new state-of-the-art on the MP-100 benchmark in the challenging 1-shot setting, marking a significant advancement in the field of category-agnostic pose estimation.","url_abs":"https://arxiv.org/abs/2411.06869v1","url_pdf":"https://arxiv.org/pdf/2411.06869v1.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":[],"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":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"large-language-model","task_name":"Large Language Model"},{"task_slug":"multimodal-large-language-model","task_name":"Multimodal Large Language Model"},{"task_slug":null,"task_name":"Open Vocabulary Keypoint Detection"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/2d-pose-estimation-on-mp-100","task":"2D Pose Estimation","dataset":"MP-100","model":"CapeLLM","rank_in_archive_order":1,"of":6,"metrics":{"Mean PCK@0.2 - 1shot":"92.60"},"uses_additional_data":false},{"leaderboard":"/sota/category-agnostic-pose-estimation-on-mp100","task":"Category-Agnostic Pose Estimation","dataset":"MP100","model":"CapeLLM","rank_in_archive_order":1,"of":5,"metrics":{"Mean PCK@0.2 - 1shot":"92.60"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}