  Text-based talking-head video editing aims to efficiently insert, delete, and
substitute segments of talking videos through a user-friendly text editing
approach. It is challenging because of \textbf{1)} generalizable talking-face
representation, \textbf{2)} seamless audio-visual transitions, and \textbf{3)}
identity-preserved talking faces. Previous works either require minutes of
talking-face video training data and expensive test-time optimization for
customized talking video editing or directly generate a video sequence without
considering in-context information, leading to a poor generalizable
representation, or incoherent transitions, or even inconsistent identity. In
this paper, we propose an efficient cascaded conditional diffusion-based
framework, which consists of two stages: audio to dense-landmark motion and
motion to video. \textit{\textbf{In the first stage}}, we first propose a
dynamic weighted in-context diffusion module to synthesize dense-landmark
motions given an edited audio. \textit{\textbf{In the second stage}}, we
introduce a warping-guided conditional diffusion module. The module first
interpolates between the start and end frames of the editing interval to
generate smooth intermediate frames. Then, with the help of the audio-to-dense
motion images, these intermediate frames are warped to obtain coarse
intermediate frames. Conditioned on the warped intermedia frames, a diffusion
model is adopted to generate detailed and high-resolution target frames, which
guarantees coherent and identity-preserved transitions. The cascaded
conditional diffusion model decomposes the complex talking editing task into
two flexible generation tasks, which provides a generalizable talking-face
representation, seamless audio-visual transitions, and identity-preserved faces
on a small dataset. Experiments show the effectiveness and superiority of the
proposed method.
