⭐Face Reenactment
Current SD adapters for face editing struggle to follow fine-grained target structure with text-based attribute control.
Motivation
Face-Adapter aims to address the unsatisfactory performance of current SD adapters
in performing face reenactment/ swapping.
in performing face reenactment/ swapping.
⭐Face Swapping
Current SD adapters for face editing struggle to generate facial detail and handle face shape changes in face swapping.
Adapter Design
⭐ Fully disentangled ID, target structure and attribute control enable 'one-model-two-tasks'.
⭐ Addressing overlooked issues.
⭐ Simple yet effective, plug and play.
More Comparison Results
Beside SD Adapters, Face-Adapter sets itself apart from
✨ Diffusion-based reenactment/ swapping methods,
by effectively using the prior of SD and improving generation quality.
✨ GAN-based reenactment/ swapping methods,
by exceled in accommodating large pose and face shape variations
and demonstrates exceptional performance in background generation.
⭐Face Reenactment
⭐Face Swapping
=== Comparison Under Large Face Shape Variations ===
=== Comparison Under Large Pose Variations ===
