Different people have different facial expressions while speaking
emotionally. A realistic facial animation system should consider such
identity-specific speaking styles and facial idiosyncrasies to achieve
high-degree of naturalness and plausibility. Existing approaches to
personalized speech-driven 3D facial animation either use one-hot identity
labels or rely-on person specific models which limit their scalability. We
present a personalized speech-driven expressive 3D facial animation synthesis
framework that models identity specific facial motion as latent representations
(called as styles), and synthesizes novel animations given a speech input with
the target style for various emotion categories. Our framework is trained in an
end-to-end fashion and has a non-autoregressive encoder-decoder architecture
with three main components: expression encoder, speech encoder and expression
decoder. Since, expressive facial motion includes both identity-specific style
and speech-related content information; expression encoder first disentangles
facial motion sequences into style and content representations, respectively.
Then, both of the speech encoder and the expression decoders input the
extracted style information to update transformer layer weights during training
phase. Our speech encoder also extracts speech phoneme label and duration
information to achieve better synchrony within the non-autoregressive synthesis
mechanism more effectively. Through detailed experiments, we demonstrate that
our approach produces temporally coherent facial expressions from input speech
while preserving the speaking styles of the target identities.

通过建模特定身份的面部动作为潜在表示（称为样式），并在各种情感类别的语音输入中合成具有目标样式的新动画，我们提出了一种个性化语音驱动的富有表现力的 3D 面部动画综合框架。

个性化的语音驱动的具有风格控制的表情生成功能的三维面部动画合成

Personalized Speech-driven Expressive 3D Facial Animation Synthesis with  Style Control

Employing deep learning-based approaches for fine-grained facial expression
analysis, such as those involving the estimation of Action Unit (AU)
intensities, is difficult due to the lack of a large-scale dataset of real
faces with sufficiently diverse AU labels for training. In this paper, we
consider how AU-level facial image synthesis can be used to substantially
augment such a dataset. We propose an AU synthesis framework that combines the
well-known 3D Morphable Model (3DMM), which intrinsically disentangles
expression parameters from other face attributes, with models that
adversarially generate 3DMM expression parameters conditioned on given target
AU labels, in contrast to the more conventional approach of generating facial
images directly. In this way, we are able to synthesize new combinations of
expression parameters and facial images from desired AU labels. Extensive
quantitative and qualitative results on the benchmark DISFA dataset demonstrate
the effectiveness of our method on 3DMM facial expression parameter synthesis
and data augmentation for deep learning-based AU intensity estimation.

本文提出了一种基于 3D 形态模型的面部图像综合框架，可以通过给定目标 AU 标签生成生成新的表情参数和面部图像来增强面部表情识别数据，并在基准 DISFA 数据集上表现出良好的增强效果和 AU 强度估计性能。