NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation
Gaze estimation is the fundamental basis for many visual tasks. Yet, the high cost of acquiring gaze datasets with 3D annotations hinders the optimization and application of gaze estimation models. In this work, we propose a novel Head-Eye redirection parametric model based on Neural Radiance Field, which allows dense gaze data generation with view consistency and accurate gaze direction. Moreover, our head-eye redirection parametric model can decouple the face and eyes for separate neural rendering, so it can achieve the purpose of separately controlling the attributes of the face, identity, illumination, and eye gaze direction. Thus diverse 3D-aware gaze datasets could be obtained by manipulating the latent code belonging to different face attributions in an unsupervised manner. Extensive experiments on several benchmarks demonstrate the effectiveness of our method in domain generalization and domain adaptation for gaze estimation tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain AdaptationDomain GeneralizationGaze EstimationNeRFNeural RenderingSimilar Papers 제목 키워드 기반
GazeNeRF: 3D-Aware Gaze Redirection with Neural Radiance Fields
We propose GazeNeRF, a 3D-aware method for the task of gaze redirection. Existing gaze redirection methods operate on 2D images and struggle to generate 3D consistent results. Instead, we build on the intuition that the …
gaze redirectionNeRFGazeGaussian: High-Fidelity Gaze Redirection with 3D Gaussian Splatting
Gaze estimation encounters generalization challenges when dealing with out-of-distribution data. To address this problem, recent methods use neural radiance fields (NeRF) to generate augmented data. However, existing met…
3DGSGaze Estimationgaze redirectionNeRFRoll Your Eyes: Gaze Redirection via Explicit 3D Eyeball Rotation
We propose a novel 3D gaze redirection framework that leverages an explicit 3D eyeball structure. Existing gaze redirection methods are typically based on neural radiance fields, which employ implicit neural representati…
Gaze EstimationUnsupervised Representation Learning for Gaze Estimation
Although automatic gaze estimation is very important to a large variety of application areas, it is difficult to train accurate and robust gaze models, in great part due to the difficulty in collecting large and diverse …
Gaze Estimationgaze redirectionHead Pose EstimationPose Estimation+13D Gaussian and Diffusion-Based Gaze Redirection
High-fidelity gaze redirection is critical for generating augmented data to improve the generalization of gaze estimators. 3D Gaussian Splatting (3DGS) models like GazeGaussian represent the state-of-the-art but can stru…