Learning-by-Synthesis for Appearance-based 3D Gaze Estimation
Inferring human gaze from low-resolution eye images is still a challenging task despite its practical importance in many application scenarios. This paper presents a learning-by-synthesis approach to accurate image-based gaze estimation that is person- and head pose-independent. Unlike existing appearance-based methods that assume person-specific training data, we use a large amount of cross-subject training data to train a 3D gaze estimator. We collect the largest and fully calibrated multi-view gaze dataset and perform a 3D reconstruction in order to generate dense training data of eye images. By using the synthesized dataset to learn a random regression forest, we show that our method outperforms existing methods that use low-resolution eye images.
Code (0)
등록된 구현이 없습니다.
Tasks
3D ReconstructionGaze EstimationregressionSimilar Papers 제목 키워드 기반
A Hierarchical Generative Model for Eye Image Synthesis and Eye Gaze Estimation
In this work, we introduce a Hierarchical Generative Model (HGM) to enable realistic forward eye image synthe- sis, as well as effective backward eye gaze estimation. The proposed HGM consists of a hierarchical generativ…
Gaze EstimationGenerative Adversarial NetworkImage GenerationLearning-by-Novel-View-Synthesis for Full-Face Appearance-Based 3D Gaze Estimation
Despite recent advances in appearance-based gaze estimation techniques, the need for training data that covers the target head pose and gaze distribution remains a crucial challenge for practical deployment. This work ex…
3D Face ReconstructionData AugmentationFace ReconstructionGaze Estimation+1Domain-Adaptive Full-Face Gaze Estimation via Novel-View-Synthesis and Feature Disentanglement
Along with the recent development of deep neural networks, appearance-based gaze estimation has succeeded considerably when training and testing within the same domain. Compared to the within-domain task, the variance of…
3D ReconstructionDisentanglementDomain AdaptationGaze Estimation+2Vulnerability of Appearance-based Gaze Estimation
Appearance-based gaze estimation has achieved significant improvement by using deep learning. However, many deep learning-based methods suffer from the vulnerability property, i.e., perturbing the raw image using noise c…
Adversarial AttackGaze EstimationAppearance-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 datas…
Gaze Estimation