We present VecFusion, a new neural architecture that can generate vector
fonts with varying topological structures and precise control point positions.
Our approach is a cascaded diffusion model which consists of a raster diffusion
model followed by a vector diffusion model. The raster model generates
low-resolution, rasterized fonts with auxiliary control point information,
capturing the global style and shape of the font, while the vector model
synthesizes vector fonts conditioned on the low-resolution raster fonts from
the first stage. To synthesize long and complex curves, our vector diffusion
model uses a transformer architecture and a novel vector representation that
enables the modeling of diverse vector geometry and the precise prediction of
control points. Our experiments show that, in contrast to previous generative
models for vector graphics, our new cascaded vector diffusion model generates
higher quality vector fonts, with complex structures and diverse styles.

我们提出了 VecFusion，这是一种新的神经架构，可以生成具有不同拓扑结构和精确控制点位置的矢量字体。通过级联扩散模型，我们的方法包括一个光栅扩散模型和一个矢量扩散模型。实验表明，与以前的矢量图形生成模型相比，我们的新的级联矢量扩散模型能够生成更高质量、具有复杂结构和多样化样式的矢量字体。

VecFusion: 矢量字体扩散生成

VecFusion: Vector Font Generation with Diffusion

Document images are now widely captured by handheld devices such as mobile
phones. The OCR performance on these images are largely affected due to
geometric distortion of the document paper, diverse camera positions and
complex backgrounds. In this paper, we propose a simple yet effective approach
to rectify distorted document image by estimating control points and reference
points. After that, we use interpolation method between control points and
reference points to convert sparse mappings to backward mapping, and remap the
original distorted document image to the rectified image. Furthermore, control
points are controllable to facilitate interaction or subsequent adjustment. We
can flexibly select post-processing methods and the number of vertices
according to different application scenarios. Experiments show that our
approach can rectify document images with various distortion types, and yield
state-of-the-art performance on real-world dataset. This paper also provides a
training dataset based on control points for document dewarping. Both the code
and the dataset are released at
this https URL

本文提出一种简单而有效的方法，通过估计控制点和参考点，在控制点和参考点之间采用插值方法，将稀疏映射转换为反向映射，并将原始扭曲的文档图像映射到矫正后的图像，从而矫正文档图像。实验表明，我们的方法能够矫正各种扭曲类型的文档图像，并在真实数据集上实现最先进的性能。文章还提供了一个用于文档去除畸变的控制点训练数据集。

控制点文档去除变形

Document Dewarping with Control Points

We propose a novel method for fitting planar B-spline curves to unorganized
data points. In traditional methods, optimization of control points and foot
points are performed in two very time-consuming steps in each iteration: 1)
control points are updated by setting up and solving a linear system of
equations; and 2) foot points are computed by projecting each data point onto a
B-spline curve. Our method uses the L-BFGS optimization method to optimize
control points and foot points simultaneously and therefore it does not need to
perform either matrix computation or foot point projection in every iteration.
As a result, our method is much faster than existing methods.

本文提出了一种新的方法，用于将平面 B 样条曲线拟合到无组织的数据点上。该方法使用 L-BFGS 优化方法同时优化控制点和足点，从而在每次迭代中不需要执行矩阵运算或足点投影，因此比现有方法快得多。