Audio-Driven Talking Face Video Generation with Dynamic Convolution Kernels
This work addresses the challenge of creating realistic talking-face videos for applications like virtual avatars or video conferencing, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of generating high-quality talking-face videos from unmatched audio and video sources by introducing dynamic convolution kernels (DCKs), achieving real-time generation at 60 fps with robustness to different identities and head postures.
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.