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Found 22 papers, 13 papers with code
GazeHTA: End-to-end Gaze Target Detection with Head-Target Association
We propose an end-to-end approach for gaze target detection: predicting a head-target connection between individuals and the target image regions they are looking at.
3D Kinematics Estimation from Video with a Biomechanical Model and Synthetic Training Data
Accurate 3D kinematics estimation of human body is crucial in various applications for human health and mobility, such as rehabilitation, injury prevention, and diagnosis, as it helps to understand the biomechanical loading experienced during movement.
Unsupervised Gaze-aware Contrastive Learning with Subject-specific Condition
Appearance-based gaze estimation has shown great promise in many applications by using a single general-purpose camera as the input device.
Investigation of Architectures and Receptive Fields for Appearance-based Gaze Estimation
With the rapid development of deep learning technology in the past decade, appearance-based gaze estimation has attracted great attention from both computer vision and human-computer interaction research communities.
Domain-Adaptive Full-Face Gaze Estimation via Novel-View-Synthesis and Feature Disentanglement
To bridge the inevitable gap between synthetic and real images, we further propose an unsupervised domain adaptation method suitable for synthetic full-face data.
EFE: End-to-end Frame-to-Gaze Estimation
In this paper, we propose a frame-to-gaze network that directly predicts both 3D gaze origin and 3D gaze direction from the raw frame out of the camera without any face or eye cropping.
Towards Single Camera Human 3D-Kinematics
Markerless estimation of 3D Kinematics has the great potential to clinically diagnose and monitor movement disorders without referrals to expensive motion capture labs; however, current approaches are limited by performing multiple de-coupled steps to estimate the kinematics of a person from videos.
GazeNeRF: 3D-Aware Gaze Redirection with Neural Radiance Fields
We propose GazeNeRF, a 3D-aware method for the task of gaze redirection.
PeCLR: Self-Supervised 3D Hand Pose Estimation from monocular RGB via Equivariant Contrastive Learning
Encouraged by the success of contrastive learning on image classification tasks, we propose a new self-supervised method for the structured regression task of 3D hand pose estimation.
Self-Learning Transformations for Improving Gaze and Head Redirection
Furthermore, we show that in the presence of limited amounts of real-world training data, our method allows for improvements in the downstream task of semi-supervised cross-dataset gaze estimation.
ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation
We show that our dataset can significantly improve the robustness of gaze estimation methods across different head poses and gaze angles.
Ranked #1 on
Gaze Estimation
on ETH-XGaze
(using extra training data)
Towards End-to-end Video-based Eye-Tracking
Estimating eye-gaze from images alone is a challenging task, in large parts due to un-observable person-specific factors.
Content-Consistent Generation of Realistic Eyes with Style
Accurately labeled real-world training data can be scarce, and hence recent works adapt, modify or generate images to boost target datasets.
Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks
In this work, we present a novel method to alleviate this problem by leveraging generative adversarial training to synthesize an eye image conditioned on a target gaze direction.
Appearance-Based Gaze Estimation via Evaluation-Guided Asymmetric Regression
Inspired by this, we design the multi-stream ARE-Net; one asymmetric regression network (AR-Net) predicts 3D gaze directions for both eyes with a novel asymmetric strategy, and the evaluation network (E-Net) adaptively adjusts the strategy by evaluating the two eyes in terms of their performance during optimization.
Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings
Conventional feature-based and model-based gaze estimation methods have proven to perform well in settings with controlled illumination and specialized cameras.
MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation
Second, we present an extensive evaluation of state-of-the-art gaze estimation methods on three current datasets, including MPIIGaze.
It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation
Eye gaze is an important non-verbal cue for human affect analysis.
Labeled pupils in the wild: A dataset for studying pupil detection in unconstrained environments
We further study the influence of image resolution, vision aids, as well as recording location (indoor, outdoor) on pupil detection performance.
Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
Images of the eye are key in several computer vision problems, such as shape registration and gaze estimation.
Appearance-Based Gaze Estimation in the Wild
Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets.
Robust Multi-resolution Pedestrian Detection in Traffic Scenes
The model contains resolution aware transformations to map pedestrians in different resolutions to a common space, where a shared detector is constructed to distinguish pedestrians from background.
