  Face-to-face communication is a common scenario including roles of speakers
and listeners. Most existing research methods focus on producing speaker
videos, while the generation of listener heads remains largely overlooked.
Responsive listening head generation is an important task that aims to model
face-to-face communication scenarios by generating a listener head video given
a speaker video and a listener head image. An ideal generated responsive
listening video should respond to the speaker with attitude or viewpoint
expressing while maintaining diversity in interaction patterns and accuracy in
listener identity information. To achieve this goal, we propose the
\textbf{M}ulti-\textbf{F}aceted \textbf{R}esponsive Listening Head Generation
Network (MFR-Net). Specifically, MFR-Net employs the probabilistic denoising
diffusion model to predict diverse head pose and expression features. In order
to perform multi-faceted response to the speaker video, while maintaining
accurate listener identity preservation, we design the Feature Aggregation
Module to boost listener identity features and fuse them with other
speaker-related features. Finally, a renderer finetuned with identity
consistency loss produces the final listening head videos. Our extensive
experiments demonstrate that MFR-Net not only achieves multi-faceted responses
in diversity and speaker identity information but also in attitude and
viewpoint expression.
