  Face attribute editing plays a pivotal role in various applications. However,
existing methods encounter challenges in achieving high-quality results while
preserving identity, editing faithfulness, and temporal consistency. These
challenges are rooted in issues related to the training pipeline, including
limited supervision, architecture design, and optimization strategy. In this
work, we introduce S3Editor, a Sparse Semantic-disentangled Self-training
framework for face video editing. S3Editor is a generic solution that
comprehensively addresses these challenges with three key contributions.
Firstly, S3Editor adopts a self-training paradigm to enhance the training
process through semi-supervision. Secondly, we propose a semantic disentangled
architecture with a dynamic routing mechanism that accommodates diverse editing
requirements. Thirdly, we present a structured sparse optimization schema that
identifies and deactivates malicious neurons to further disentangle impacts
from untarget attributes. S3Editor is model-agnostic and compatible with
various editing approaches. Our extensive qualitative and quantitative results
affirm that our approach significantly enhances identity preservation, editing
fidelity, as well as temporal consistency.
