  Recently, talking face generation has drawn ever-increasing attention from
the research community in computer vision due to its arduous challenges and
widespread application scenarios, e.g. movie animation and virtual anchor.
Although persevering efforts have been undertaken to enhance the fidelity and
lip-sync quality of generated talking face videos, there is still large room
for further improvements of synthesis quality and efficiency. Actually, these
attempts somewhat ignore the explorations of fine-granularity feature
extraction/integration and the consistency between probability distributions of
landmarks, thereby recurring the issues of local details blurring and degraded
fidelity. To mitigate these dilemmas, in this paper, a novel CLIP-based
Attention and Probability Map Guided Network (CPNet) is delicately designed for
inferring high-fidelity talking face videos. Specifically, considering the
demands of fine-grained feature recalibration, a clip-based attention condenser
is exploited to transfer knowledge with rich semantic priors from the
prevailing CLIP model. Moreover, to guarantee the consistency in probability
space and suppress the landmark ambiguity, we creatively propose the density
map of facial landmark as auxiliary supervisory signal to guide the landmark
distribution learning of generated frame. Extensive experiments on the
widely-used benchmark dataset demonstrate the superiority of our CPNet against
state of the arts in terms of image and lip-sync quality. In addition, a cohort
of studies are also conducted to ablate the impacts of the individual pivotal
components.
