Generating Persuasive Visual Storylines for Promotional Videos

TL;DR

This paper introduces WundtBackpack, an AI algorithm that automatically generates persuasive visual storylines for promotional videos, significantly improving perceived persuasiveness and potential revenue over existing methods.

Contribution

The paper presents a novel approach combining a learnable persuasiveness evaluation and a clustering-based algorithm to automate the creation of persuasive promotional video sequences.

Findings

Achieves nearly 10% higher perceived persuasiveness scores by humans.

Attains 12.5% higher expected revenue compared to state-of-the-art methods.

Effectively organizes visual materials into persuasive sequences with limited data.

Abstract

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.