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

Understand

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.