With the development of deep neural networks, the demand for a significant
amount of annotated training data becomes the performance bottlenecks in many
fields of research and applications. Image synthesis can generate annotated
images automatically and freely, which gains increasing attention recently. In
this paper, we propose to synthesize scene text images from the 3D virtual
worlds, where the precise descriptions of scenes, editable
illumination/visibility, and realistic physics are provided. Different from the
previous methods which paste the rendered text on static 2D images, our method
can render the 3D virtual scene and text instances as an entirety. In this way,
real-world variations, including complex perspective transformations, various
illuminations, and occlusions, can be realized in our synthesized scene text
images. Moreover, the same text instances with various viewpoints can be
produced by randomly moving and rotating the virtual camera, which acts as
human eyes. The experiments on the standard scene text detection benchmarks
using the generated synthetic data demonstrate the effectiveness and
superiority of the proposed method. The code and synthetic data is available
at: this https URL

本文提出了一种利用 3D 虚拟世界合成场景文本图像的方法，相较之前的方法，此方法可以将 3D 虚拟场景和文本实例作为一个整体进行渲染，并通过随机移动和旋转虚拟摄像机来生成多个视角的相同文本实例，最终实现生成的场景文本图像在场景、光照、遮挡等方面的多样性，得到的生成数据在标准场景文本检测基准测试中表现更加有效和优越。