  Video contents have become a critical tool for promoting products in
E-commerce. However, the lack of automatic promotional video generation
solutions makes large-scale video-based promotion campaigns infeasible. The
first step of automatically producing promotional videos is to generate visual
storylines, which is to select the building block footage and place them in an
appropriate order. This task is related to the subjective viewing experience.
It is hitherto performed by human experts and thus, hard to scale. To address
this problem, we propose WundtBackpack, an algorithmic approach to generate
storylines based on available visual materials, which can be video clips or
images. It consists of two main parts, 1) the Learnable Wundt Curve to evaluate
the perceived persuasiveness based on the stimulus intensity of a sequence of
visual materials, which only requires a small volume of data to train; and 2) a
clustering-based backpacking algorithm to generate persuasive sequences of
visual materials while considering video length constraints. In this way, the
proposed approach provides a dynamic structure to empower artificial
intelligence (AI) to organize video footage in order to construct a sequence of
visual stimuli with persuasive power. Extensive real-world experiments show
that our approach achieves close to 10% higher perceived persuasiveness scores
by human testers, and 12.5% higher expected revenue compared to the best
performing state-of-the-art approach.
