Depth Structure Preserving Scene Image Generation

Wendong Zhang, Feng Gao, Bingbing Ni, Ling‐Yu Duan, Yichao Yan, Jingwei Xu, Xiaokang Yang

Abstract

Key to automatically generate natural scene images is to properly arrange amongst various spatial elements, especially in the depth cue. To this end, we introduce a novel depth structure preserving scene image generation network (DSP-GAN), which favors a hierarchical architecture, for the purpose of depth structure preserving scene image generation. The main trunk of the proposed infrastructure is built upon a Hawkes point process that models high-order 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 annotated natural scene images collected from SUN dataset and demonstrate that our models are capable of generating depth-realistic natural scene image.

Locations

• arXiv (Cornell University) - View - PDF
• Proceedings of the 30th ACM International Conference on Multimedia - View