The goal of text spotting is to perform text detection and recognition in an
end-to-end manner. Although the diversity of luminosity and orientation in
scene texts has been widely studied, the font diversity and shape variance of
the same character are ignored in recent works, since most characters in
natural images are rendered in standard fonts. To solve this problem, we
present a Chinese Artistic Dataset, termed as ARText, which contains 33,000
artistic images with rich shape deformation and font diversity. Based on this
database, we develop a deformation robust text spotting method (DR TextSpotter)
to solve the recognition problem of complex deformation of characters in
different fonts. Specifically, we propose a geometric prior module to highlight
the important features based on the unsupervised landmark detection
sub-network. A graph convolution network is further constructed to fuse the
character features and landmark features, and then performs semantic reasoning
to enhance the discrimination for different characters. The experiments are
conducted on ARText and IC19-ReCTS datasets. Our results demonstrate the
effectiveness of our proposed method.

通过构建具有形状变形和字体多样性的中国艺术数据集，提出了一种变形鲁棒的文本定位方法 (DR TextSpotter)，该方法利用几何先验模块和图卷积网络融合字符特征和标记特征，并进行语义推理以增强对不同字符的区分，实验证明了该方法的有效性。

具有几何先验的变形鲁棒文本识别

Deformation Robust Text Spotting with Geometric Prior

We present a new application direction named Pareidolia Face Reenactment,
which is defined as animating a static illusory face to move in tandem with a
human face in the video. For the large differences between pareidolia face
reenactment and traditional human face reenactment, two main challenges are
introduced, i.e., shape variance and texture variance. In this work, we propose
a novel Parametric Unsupervised Reenactment Algorithm to tackle these two
challenges. Specifically, we propose to decompose the reenactment into three
catenate processes: shape modeling, motion transfer and texture synthesis. With
the decomposition, we introduce three crucial components, i.e., Parametric
Shape Modeling, Expansionary Motion Transfer and Unsupervised Texture
Synthesizer, to overcome the problems brought by the remarkably variances on
pareidolia faces. Extensive experiments show the superior performance of our
method both qualitatively and quantitatively. Code, model and data are
available on our project page.

提出了新的应用方向 —— Pareidolia Face Reenactment，利用新颖的参数无监督再现算法实现了对运动视频静态幻觉面部的模拟，有效解决了面部形状和纹理方差问题。