Depth Structure Preserving Scene Image Generation
This addresses scene image generation for computer vision applications, but appears incremental as it builds on existing GAN and point process methods.
The paper tackles the problem of generating natural scene images by properly arranging spatial elements in depth, introducing DSP-GAN to preserve depth structure, and demonstrates its capability to generate depth-realistic images on a SUN dataset subset.
Key to automatically generate natural scene images is to properly arrange among various spatial elements, especially in the depth direction. To this end, we introduce a novel depth structure preserving scene image generation network (DSP-GAN), which favors a hierarchical and heterogeneous architecture, for the purpose of depth structure preserving scene generation. The main trunk of the proposed infrastructure is built on a Hawkes point process that models the spatial dependency between different depth layers. Within each layer generative adversarial sub-networks are trained collaboratively to generate realistic scene components, conditioned on the layer information produced by the point process. We experiment our model on a sub-set of SUNdataset with annotated scene images and demonstrate that our models are capable of generating depth-realistic natural scene image.