A Discrete Event Approach for Scene Generation conditioned on Natural Language Recognizable scene synthesis conditioned on natural language (NL) descriptions has many applications. The success of existing methods is limited to either clipart scenes or single objects. In this paper, a novel approach to generate realistic complex scenes is proposed. Instead of using a local image asset library or direct texttopixel mappings, this method deploys knowledge retrieved from the Internet, which helps to generate diverse and creative scenes. The proposed approach allows to modify the synthesized scene via NL. This helps to generate more complex scenes, and to correct training bias or errors. We validate the proposed approach on challenging MSCOCO dataset and present that our method can improve the semantic alignment to the input language descriptions, and the interpretability and quality of synthesized scenes.

SynthText3D: synthesizing scene text images from 3D virtual worlds 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 illuminationvisibility, 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, realworld 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.