  As a key component of talking face generation, lip movements generation
determines the naturalness and coherence of the generated talking face video.
Prior literature mainly focuses on speech-to-lip generation while there is a
paucity in text-to-lip (T2L) generation. T2L is a challenging task and existing
end-to-end works depend on the attention mechanism and autoregressive (AR)
decoding manner. However, the AR decoding manner generates current lip frame
conditioned on frames generated previously, which inherently hinders the
inference speed, and also has a detrimental effect on the quality of generated
lip frames due to error propagation. This encourages the research of parallel
T2L generation. In this work, we propose a parallel decoding model for fast and
high-fidelity text-to-lip generation (ParaLip). Specifically, we predict the
duration of the encoded linguistic features and model the target lip frames
conditioned on the encoded linguistic features with their duration in a
non-autoregressive manner. Furthermore, we incorporate the structural
similarity index loss and adversarial learning to improve perceptual quality of
generated lip frames and alleviate the blurry prediction problem. Extensive
experiments conducted on GRID and TCD-TIMIT datasets demonstrate the
superiority of proposed methods. Video samples are available via
\url{https://paralip.github.io/}.
