  Talking face generation technology creates talking videos from arbitrary
appearance and motion signal, with the "arbitrary" offering ease of use but
also introducing challenges in practical applications. Existing methods work
well with standard inputs but suffer serious performance degradation with
intricate real-world ones. Moreover, efficiency is also an important concern in
deployment. To comprehensively address these issues, we introduce SuperFace, a
teacher-student framework that balances quality, robustness, cost and
editability. We first propose a simple but effective teacher model capable of
handling inputs of varying qualities to generate high-quality results. Building
on this, we devise an efficient distillation strategy to acquire an
identity-specific student model that maintains quality with significantly
reduced computational load. Our experiments validate that SuperFace offers a
more comprehensive solution than existing methods for the four mentioned
objectives, especially in reducing FLOPs by 99\% with the student model.
SuperFace can be driven by both video and audio and allows for localized facial
attributes editing.
