To enable realistic shape (e.g. pose and expression) transfer, existing face
reenactment methods rely on a set of target faces for learning subject-specific
traits. However, in real-world scenario end-users often only have one target
face at hand, rendering existing methods inapplicable. In this work, we bridge
this gap by proposing a novel one-shot face reenactment learning framework. Our
key insight is that the one-shot learner should be able to disentangle and
compose appearance and shape information for effective modeling. Specifically,
the target face appearance and the source face shape are first projected into
latent spaces with their corresponding encoders. Then these two latent spaces
are associated by learning a shared decoder that aggregates multi-level
features to produce the final reenactment results. To further improve the
synthesizing quality on mustache and hair regions, we additionally propose
FusionNet which combines the strengths of our learned decoder and the
traditional warping method. Extensive experiments show that our one-shot face
reenactment system achieves superior transfer fidelity as well as identity
preserving capability than alternatives. More remarkably, our approach trained
with only one target image per subject achieves competitive results to those
using a set of target images, demonstrating the practical merit of this work.
Code, models and an additional set of reenacted faces have been publicly
released at the project page.

该研究提出了一种基于一次学习的人脸复现系统，通过将目标人脸外观和源脸形状投影到潜在空间中，并学习一个共享解码器来使这两个潜在空间相关联，从而实现有效建模。该方法在 Mustache 和 Hair 区域照片合成的质量上比传统方法有很大提高，并且仅用一张目标图像训练便能实现与多张目标图像训练同样令人满意的结果.

一拍即合人脸再现

One-shot Face Reenactment

This paper introduces a new neural structure called FusionNet, which extends
existing attention approaches from three perspectives. First, it puts forward a
novel concept of "history of word" to characterize attention information from
the lowest word-level embedding up to the highest semantic-level
representation. Second, it introduces an improved attention scoring function
that better utilizes the "history of word" concept. Third, it proposes a
fully-aware multi-level attention mechanism to capture the complete information
in one text (such as a question) and exploit it in its counterpart (such as
context or passage) layer by layer. We apply FusionNet to the Stanford Question
Answering Dataset (SQuAD) and it achieves the first position for both single
and ensemble model on the official SQuAD leaderboard at the time of writing
(Oct. 4th, 2017). Meanwhile, we verify the generalization of FusionNet with two
adversarial SQuAD datasets and it sets up the new state-of-the-art on both
datasets: on AddSent, FusionNet increases the best F1 metric from 46.6% to
51.4%; on AddOneSent, FusionNet boosts the best F1 metric from 56.0% to 60.7%.

本文介绍了一种名为 FusionNet 的新型神经结构，它通过三个方面的创新来扩展现有的注意力方法。我们将 FusionNet 应用于斯坦福问答数据集（SQuAD）中，并在官方 SQuAD 排行榜上取得了单一模型和集成模型的第一名。同时，我们验证了 FusionNet 的泛化性，并且它在两个对抗性 SQuAD 数据集上取得了新的最高水平。