  Lip sync is a fundamental audio-visual task. However, existing lip sync
methods fall short of being robust to the incredible diversity of videos taken
in the wild, and the majority of the diversity is caused by compound
distracting factors that could degrade existing lip sync methods. To address
these issues, this paper proposes a data standardization pipeline that can
produce standardized expressive images while preserving lip motion information
from the input and reducing the effects of compound distracting factors. Based
on recent advances in 3D face reconstruction, we first create a model that can
consistently disentangle expressions, with lip motion information embedded.
Then, to reduce the effects of compound distracting factors on synthesized
images, we synthesize images with only expressions from the input,
intentionally setting all other attributes at predefined values independent of
the input. Using synthesized images, existing lip sync methods improve their
data efficiency and robustness, and they achieve competitive performance for
the active speaker detection task.
