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Gaze estimation is the fundamental basis for many visual tasks. Yet, the highcost of acquiring gaze datasets with 3D annotations hinders the optimizationand application of gaze estimation models. In this work, we propose a novelHead-Eye redirection parametric model based on Neural Radiance Field, whichallows dense gaze data generation with view consistency and accurate gazedirection. Moreover, our head-eye redirection parametric model can decouple theface and eyes for separate neural rendering, so it can achieve the purpose ofseparately controlling the attributes of the face, identity, illumination, andeye gaze direction. Thus diverse 3D-aware gaze datasets could be obtained bymanipulating the latent code belonging to different face attributions in anunsupervised manner. Extensive experiments on several benchmarks demonstratethe effectiveness of our method in domain generalization and domain adaptationfor gaze estimation tasks.

NeRF-Gaze: A Head-Eye Redirection Parametric Model for Gaze Estimation