{"url":"/method/fcpose","slug":"fcpose","name":"FCPose","full_name":"FCPose","full_name_withheld":false,"description_markdown":"**FCPose** is a fully convolutional multi-person [pose estimation framework](https://paperswithcode.com/methods/category/pose-estimation-models) using dynamic instance-aware convolutions. Different from existing methods, which often require ROI (Region of Interest) operations and/or grouping post-processing, FCPose eliminates the ROIs and grouping pre-processing with dynamic instance aware keypoint estimation heads. The dynamic keypoint heads are conditioned on each instance (person), and can encode the instance concept in the dynamically-generated weights of their filters. \r\n\r\nOverall, FCPose is built upon the one-stage object detector [FCOS](https://paperswithcode.com/method/fcos). The controller that generates the weights of the keypoint heads is attached to the FCOS heads. The weights $\\theta\\_{i}$ generated by the controller is used to fulfill the keypoint head $f$ for the instance $i$. Moreover, a keypoint refinement module is introduced to predict the offsets from each location of the heatmaps to the ground-truth keypoints. Finally, the coordinates derived from the predicted heatmaps are refined by the offsets predicted by the keypoint refinement module, resulting in the final keypoint results. \"Rel. coord.\" is a map of the relative coordinates from all the locations of the feature maps $F$ to the location where the weights are generated. The relative coordinate map is concatenated to $F$ as the input to the keypoint head.","description_state":"present","introduced_year":null,"introduced_by":{"title":"FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions","paper":"/paper/fcpose-fully-convolutional-multi-person-pose","first_author":"Weian Mao","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/fcpose-fully-convolutional-multi-person-pose"},"source":{"url":"https://arxiv.org/abs/2105.14185v1","title":"FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"Pose Estimation Models","url":"/methods/category/pose-estimation-models","pwc_aliases":[]}],"n_papers_tagged":1,"archive_num_papers":1,"papers_newest_first":[{"paper":"/paper/fcpose-fully-convolutional-multi-person-pose","title":"FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions","date":"2021-05-29","arxiv_id":"2105.14185","n_code_links":3,"syntology":null}],"papers_shown":1,"tasks":[{"task":"/task/keypoint-estimation","name":"Keypoint Estimation","papers":1},{"task":"/task/multi-person-pose-estimation","name":"Multi-Person Pose Estimation","papers":1},{"task":"/task/pose-estimation","name":"Pose Estimation","papers":1}],"tasks_shown":3,"n_tasks":3,"usage_by_year":[{"year":"2021","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/fcpose"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}