  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: https://github.com/MhLiao/SynthText3D
