  Despite numerous completed studies, achieving high fidelity talking face
generation with highly synchronized lip movements corresponding to arbitrary
audio remains a significant challenge in the field. The shortcomings of
published studies continue to confuse many researchers. This paper introduces
G4G, a generic framework for high fidelity talking face generation with
fine-grained intra-modal alignment. G4G can reenact the high fidelity of
original video while producing highly synchronized lip movements regardless of
given audio tones or volumes. The key to G4G's success is the use of a diagonal
matrix to enhance the ordinary alignment of audio-image intra-modal features,
which significantly increases the comparative learning between positive and
negative samples. Additionally, a multi-scaled supervision module is introduced
to comprehensively reenact the perceptional fidelity of original video across
the facial region while emphasizing the synchronization of lip movements and
the input audio. A fusion network is then used to further fuse the facial
region and the rest. Our experimental results demonstrate significant
achievements in reenactment of original video quality as well as highly
synchronized talking lips. G4G is an outperforming generic framework that can
produce talking videos competitively closer to ground truth level than current
state-of-the-art methods.
