DynamicFace: High-Quality and Consistent Video Face Swapping using Composable 3D Facial Priors
Journal:
arXiv
Published Date:
Jan 15, 2025
Abstract
Face swapping transfers the identity of a source face to a target face while
retaining the attributes like expression, pose, hair, and background of the
target face. Advanced face swapping methods have achieved attractive results.
However, these methods often inadvertently transfer identity information from
the target face, compromising expression-related details and accurate identity.
We propose a novel method DynamicFace that leverages the power of diffusion
model and plug-and-play temporal layers for video face swapping. First, we
introduce four fine-grained face conditions using 3D facial priors. All
conditions are designed to be disentangled from each other for precise and
unique control. Then, we adopt Face Former and ReferenceNet for high-level and
detailed identity injection. Through experiments on the FF++ dataset, we
demonstrate that our method achieves state-of-the-art results in face swapping,
showcasing superior image quality, identity preservation, and expression
accuracy. Besides, our method could be easily transferred to video domain with
temporal attention layer. Our code and results will be available on the project
page: https://dynamic-face.github.io/
