CreaGAN: An Automatic Creative Generation Framework for Display Advertising

Shiyao Wang, Qi Liu, Yicheng Zhong, Zhilong Zhou, Tiezheng Ge, Defu Lian, Yuning Jiang

Abstract: Creatives are an effective form of delivering product information on the E-commerce platform. Designing an exquisite creative is a time-consuming but crucial task for sellers. In order to accelerate the process, we propose an automatic creative generation framework, named CreaGAN, to make the design procedure easier, faster, and more accurate. Given a well-designed creative for one product, our method can generalize to other product materials by utilizing existing design elements (e.g., background material). The framework consists of two major parts: aesthetics-aware placement and creative inpainting model. The placement model aims to generate plausible locations for new products by considering aesthetic principles. And the inpainting model focus on filling the mismatched regions through contextual information. We conduct experiments on both the public dataset and the real-world creative dataset. Quantitative and qualitative results demonstrate that our method outperforms current state-of-the-art methods and obtains more reasonable and aesthetic visualization results.