  Animating high-fidelity video portrait with speech audio is crucial for
virtual reality and digital entertainment. While most previous studies rely on
accurate explicit structural information, recent works explore the implicit
scene representation of Neural Radiance Fields (NeRF) for realistic generation.
In order to capture the inconsistent motions as well as the semantic difference
between human head and torso, some work models them via two individual sets of
NeRF, leading to unnatural results. In this work, we propose Semantic-aware
Speaking Portrait NeRF (SSP-NeRF), which creates delicate audio-driven
portraits using one unified set of NeRF. The proposed model can handle the
detailed local facial semantics and the global head-torso relationship through
two semantic-aware modules. Specifically, we first propose a Semantic-Aware
Dynamic Ray Sampling module with an additional parsing branch that facilitates
audio-driven volume rendering. Moreover, to enable portrait rendering in one
unified neural radiance field, a Torso Deformation module is designed to
stabilize the large-scale non-rigid torso motions. Extensive evaluations
demonstrate that our proposed approach renders more realistic video portraits
compared to previous methods. Project page:
https://alvinliu0.github.io/projects/SSP-NeRF
