Over the past years, a substantial amount of work has been done on the
problem of facial reenactment, with the solutions coming mainly from the
graphics community. Head reenactment is an even more challenging task, which
aims at transferring not only the facial expression, but also the entire head
pose from a source person to a target. Current approaches either train
person-specific systems, or use facial landmarks to model human heads, a
representation that might transfer unwanted identity attributes from the source
to the target. We propose head2headFS, a novel easily adaptable pipeline for
head reenactment. We condition synthesis of the target person on dense 3D face
shape information from the source, which enables high quality expression and
pose transfer. Our video-based rendering network is fine-tuned under a few-shot
learning strategy, using only a few samples. This allows for fast adaptation of
a generic generator trained on a multiple-person dataset, into a
person-specific one.

提出了一种新颖的轻松适应管道 head2headFS，可以将源人的表情和整个头部姿势高质量地转移到目标人，使用源的密集 3D 面部形状信息来调节目标人的合成，fine-tuning 自适应多人数据集训练的通用生成器为个人特定的生成器，具有 few-shot learning 功能。