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Category-Agnostic Pose Estimation

Category-Agnostic Pose Estimation (CAPE) 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.

Papers

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Showing 1–15 of 15 papers

TitleStatusHype
Recurrent Feature Mining and Keypoint Mixup Padding for Category-Agnostic Pose EstimationCode1
Edge Weight Prediction For Category-Agnostic Pose EstimationCode1
CapeLLM: Support-Free Category-Agnostic Pose Estimation with Multimodal Large Language Models—0
SCAPE: A Simple and Strong Category-Agnostic Pose EstimatorCode0
CapeX: Category-Agnostic Pose Estimation from Textual Point ExplanationCode1
Meta-Point Learning and Refining for Category-Agnostic Pose EstimationCode1
Category-Agnostic Pose Estimation for Point Clouds—0
ESCAPE: Encoding Super-keypoints for Category-Agnostic Pose EstimationCode0
Dynamic Support Information Mining for Category-Agnostic Pose Estimation—0
A Graph-Based Approach for Category-Agnostic Pose EstimationCode2
Matching Is Not Enough: A Two-Stage Framework for Category-Agnostic Pose EstimationCode1
Pose for Everything: Towards Category-Agnostic Pose EstimationCode2
Revisiting Fine-tuning for Few-shot Learning—0
Prototypical Networks for Few-shot LearningCode2
Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksCode1
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