  In this paper, we present a dynamic convolution kernel (DCK) strategy for
convolutional neural networks. Using a fully convolutional network with the
proposed DCKs, high-quality talking-face video can be generated from
multi-modal sources (i.e., unmatched audio and video) in real time, and our
trained model is robust to different identities, head postures, and input
audios. Our proposed DCKs are specially designed for audio-driven talking face
video generation, leading to a simple yet effective end-to-end system. We also
provide a theoretical analysis to interpret why DCKs work. Experimental results
show that our method can generate high-quality talking-face video with
background at 60 fps. Comparison and evaluation between our method and the
state-of-the-art methods demonstrate the superiority of our method.
