Category-Agnostic Pose Estimation
4 papers with code • 0 benchmarks • 0 datasets
Benchmarks
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Most implemented papers
Pose for Everything: Towards Category-Agnostic Pose Estimation
luminxu/pose-for-everything
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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.
Matching Is Not Enough: A Two-Stage Framework for Category-Agnostic Pose Estimation
flyinglynx/capeformer
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CVPR 2023
To calibrate the inaccurate matching results, we introduce a two-stage framework, where matched keypoints from the first stage are viewed as similarity-aware position proposals.
Pose Anything: A Graph-Based Approach for Category-Agnostic Pose Estimation
This approach not only enables object pose generation based on arbitrary keypoint definitions but also significantly reduces the associated costs, paving the way for versatile and adaptable pose estimation applications.
Meta-Point Learning and Refining for Category-Agnostic Pose Estimation
Existing methods only rely on the features extracted at support keypoints to predict or refine the keypoints on query image, but a few support feature vectors are local and inadequate for CAPE.
