28 papers with code • 1 benchmarks • 1 datasets
These leaderboards are used to track progress in Keypoint Estimation
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Most implemented papers
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
Objects as Points
We model an object as a single point --- the center point of its bounding box.
SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation
Estimating 3D orientation and translation of objects is essential for infrastructure-less autonomous navigation and driving.
FCPose: Fully Convolutional Multi-Person Pose Estimation with Dynamic Instance-Aware Convolutions
We propose a fully convolutional multi-person pose estimation framework using dynamic instance-aware convolutions, termed FCPose.
Bottom-up Object Detection by Grouping Extreme and Center Points
With the advent of deep learning, object detection drifted from a bottom-up to a top-down recognition problem.
KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects
We address two problems: first, we establish an easy method for capturing and labeling 3D keypoints on desktop objects with an RGB camera; and second, we develop a deep neural network, called $KeyPose$, that learns to accurately predict object poses using 3D keypoints, from stereo input, and works even for transparent objects.
CenterNet3D: An Anchor Free Object Detector for Point Cloud
However, because inherent sparsity of point clouds, 3D object center points are likely to be in empty space which makes it difficult to estimate accurate boundaries.
Whole-Body Human Pose Estimation in the Wild
This paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet.
HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation
We show that HybrIK preserves both the accuracy of 3D pose and the realistic body structure of the parametric human model, leading to a pixel-aligned 3D body mesh and a more accurate 3D pose than the pure 3D keypoint estimation methods.
Single Image 3D Interpreter Network
In this work, we propose 3D INterpreter Network (3D-INN), an end-to-end framework which sequentially estimates 2D keypoint heatmaps and 3D object structure, trained on both real 2D-annotated images and synthetic 3D data.