  In this work, we present a new single-stage method for subject agnostic face
swapping and identity transfer, named FaceDancer. We have two major
contributions: Adaptive Feature Fusion Attention (AFFA) and Interpreted Feature
Similarity Regularization (IFSR). The AFFA module is embedded in the decoder
and adaptively learns to fuse attribute features and features conditioned on
identity information without requiring any additional facial segmentation
process. In IFSR, we leverage the intermediate features in an identity encoder
to preserve important attributes such as head pose, facial expression,
lighting, and occlusion in the target face, while still transferring the
identity of the source face with high fidelity. We conduct extensive
quantitative and qualitative experiments on various datasets and show that the
proposed FaceDancer outperforms other state-of-the-art networks in terms of
identityn transfer, while having significantly better pose preservation than
most of the previous methods.
