  In this paper, we present the decomposed triplane-hash neural radiance fields
(DT-NeRF), a framework that significantly improves the photorealistic rendering
of talking faces and achieves state-of-the-art results on key evaluation
datasets. Our architecture decomposes the facial region into two specialized
triplanes: one specialized for representing the mouth, and the other for the
broader facial features. We introduce audio features as residual terms and
integrate them as query vectors into our model through an audio-mouth-face
transformer. Additionally, our method leverages the capabilities of Neural
Radiance Fields (NeRF) to enrich the volumetric representation of the entire
face through additive volumetric rendering techniques. Comprehensive
experimental evaluations corroborate the effectiveness and superiority of our
proposed approach.
