  This paper presents the first significant work on directly predicting 3D face
landmarks on neural radiance fields (NeRFs). Our 3D coarse-to-fine Face
Landmarks NeRF (FLNeRF) model efficiently samples from a given face NeRF with
individual facial features for accurate landmarks detection. Expression
augmentation is applied to facial features in a fine scale to simulate large
emotions range including exaggerated facial expressions (e.g., cheek blowing,
wide opening mouth, eye blinking) for training FLNeRF. Qualitative and
quantitative comparison with related state-of-the-art 3D facial landmark
estimation methods demonstrate the efficacy of FLNeRF, which contributes to
downstream tasks such as high-quality face editing and swapping with direct
control using our NeRF landmarks. Code and data will be available. Github link:
https://github.com/ZHANG1023/FLNeRF.
