Head Pose Estimation
47 papers with code • 9 benchmarks • 10 datasets
Estimating the head pose of a person is a crucial problem that has a large amount of applications such as aiding in gaze estimation, modeling attention, fitting 3D models to video and performing face alignment.
( Image credit: FSA-Net: Learning Fine-Grained Structure Aggregation for Head Pose Estimation from a Single Image )
Benchmarks
These leaderboards are used to track progress in Head Pose Estimation
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Libraries
Use these libraries to find Head Pose Estimation models and implementations
AFLW
AFLW2000-3D
Panoptic
COFW
WFLW
BIWI
Biwi Kinect Head Pose
HPD
Most implemented papers
HoloFace: Augmenting Human-to-Human Interactions on HoloLens
Head pose estimation is accomplished by fitting a deformable 3D model to the landmarks localized using face alignment.
PROPEL: Probabilistic Parametric Regression Loss for Convolutional Neural Networks
masadcv/PROPEL
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We propose a PRObabilistic Parametric rEgression Loss (PROPEL) that facilitates CNNs to learn parameters of probability distributions for addressing probabilistic regression problems.
Nose, eyes and ears: Head pose estimation by locating facial keypoints
Arnav0400/Headpose_Estimation
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Monocular head pose estimation requires learning a model that computes the intrinsic Euler angles for pose (yaw, pitch, roll) from an input image of human face.
Hybrid coarse-fine classification for head pose estimation
haofanwang/accurate-head-pose
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In this paper, to do the estimation without facial landmarks, we combine the coarse and fine regression output together for a deep network.
Improving Head Pose Estimation with a Combined Loss and Bounding Box Margin Adjustment
We address a problem of estimating pose of a person's head from its RGB image.
FSA-Net: Learning Fine-Grained Structure Aggregation for Head Pose Estimation From a Single Image
shamangary/FSA-Net
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CVPR 2019
Our method is based on regression and feature aggregation.
Energy-Based Models for Deep Probabilistic Regression
fregu856/ebms_regression
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ECCV 2020
In our proposed approach, we create an energy-based model of the conditional target density p(y|x), using a deep neural network to predict the un-normalized density from (x, y).
RankPose: Learning Generalised Feature with Rank Supervision for Head Pose Estimation
We address the challenging problem of RGB image-based head pose estimation.
Deep Ordinal Regression with Label Diversity
By discretizing the target into a set of non-overlapping classes, it has been shown that training a classifier can improve neural network accuracy compared to using a standard regression approach.
RealHePoNet: a robust single-stage ConvNet for head pose estimation in the wild
In this work, we address this problem, defined here as the estimation of both vertical (tilt/pitch) and horizontal (pan/yaw) angles, through the use of a single Convolutional Neural Network (ConvNet) model, trying to balance precision and inference speed in order to maximize its usability in real-world applications.
