Facial style transfer has been quite popular among researchers due to the
rise of emerging technologies such as eXtended Reality (XR), Metaverse, and
Non-Fungible Tokens (NFTs). Furthermore, StyleGAN methods along with
transfer-learning strategies have reduced the problem of limited data to some
extent. However, most of the StyleGAN methods overfit the styles while adding
artifacts to facial images. In this paper, we propose a facial pose awareness
and style transfer (Face-PAST) network that preserves facial details and
structures while generating high-quality stylized images. Dual StyleGAN
inspires our work, but in contrast, our work uses a pre-trained style
generation network in an external style pass with a residual modulation block
instead of a transform coding block. Furthermore, we use the gated mapping unit
and facial structure, identity, and segmentation losses to preserve the facial
structure and details. This enables us to train the network with a very limited
amount of data while generating high-quality stylized images. Our training
process adapts curriculum learning strategy to perform efficient and flexible
style mixing in the generative space. We perform extensive experiments to show
the superiority of Face-PAST in comparison to existing state-of-the-art
methods.

我们提出了一种面部姿态感知和风格转换（Face-PAST）网络，它在生成高质量的风格化图像的同时保留面部细节和结构。通过使用预训练的风格生成网络和残留调制块而非变换编码块，以及利用门控映射单元和面部结构、身份和分割损失来保留面部结构和细节，我们的网络能够用非常有限的数据进行训练。我们的训练过程采用课程学习策略来在生成空间中进行高效灵活的风格混合。通过广泛的实验证明了我们的 Face-PAST 方法相较于现有最先进的方法的优越性。