Rendering Sketches Interactive rendering generation from sketches using conditional generative adversarial neural network

Abstract

Architects use sketches in the early design phase to organize and elaborate their initial ideas, and those initial sketches often support ideation for the final design. However, the sketches in the early design phase tend to be abstract and hard to interpret. Minimal prior works provide tools for quick visualization of the initial sketch. This study provides a scheme for architects and designers to generate preliminary renderings in the early design stage. In this study, we use conditional generative adversarial networks (cGAN) as the frame and introduces an updater network to the existing cGAN to support the iterative design process. A sketch serves as input to see the rendering and update the sketch based on the generated renderings by adding more resolution and details. The network is able to generate a reasonable rendering from the single-image network, and is able to update the renderings iteratively via the updater network. The dataset is collected from residential buildings exclusively, but the architectural categories can be expanded to other types of buildings in the future. Results show that the proposed scheme is able to provide reasonable renderings from sketches, and the generated rendering can be updated with a higher level of details within a second if the user provides a more detailed sketch. The contribution of this study includes introducing an updater network to the existing algorithm to enable iterative input and provides an alternative enhancement approach to the resolution of the generated image.