  Audio-guided face reenactment aims to generate a photorealistic face that has
matched facial expression with the input audio. However, current methods can
only reenact a special person once the model is trained or need extra
operations such as 3D rendering and image post-fusion on the premise of
generating vivid faces. To solve the above challenge, we propose a novel
\emph{R}eal-time \emph{A}udio-guided \emph{M}ulti-face reenactment approach
named \emph{APB2FaceV2}, which can reenact different target faces among
multiple persons with corresponding reference face and drive audio signal as
inputs. Enabling the model to be trained end-to-end and have a faster speed, we
design a novel module named Adaptive Convolution (AdaConv) to infuse audio
information into the network, as well as adopt a lightweight network as our
backbone so that the network can run in real time on CPU and GPU. Comparison
experiments prove the superiority of our approach than existing
state-of-the-art methods, and further experiments demonstrate that our method
is efficient and flexible for practical applications
https://github.com/zhangzjn/APB2FaceV2
