Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/179066
Title: AI-empowered persuasive video generation: a survey
Authors: Liu, Chang
Yu, Han
Keywords: Computer and Information Science
Issue Date: 2023
Source: Liu, C. & Yu, H. (2023). AI-empowered persuasive video generation: a survey. ACM Computing Surveys, 55(13s), 285-. https://dx.doi.org/10.1145/3588764
Project: AISG2-RP-2020-019 
A20G8b0102. 
FCP-NTURG-2021-014 
Journal: ACM Computing Surveys 
Abstract: Promotional videos are rapidly becoming a popular medium for persuading people to change their behaviours in many settings (e.g., online shopping, social enterprise initiatives). Today, such videos are often produced by professionals, which is a time-, labour- and cost-intensive undertaking. In order to produce such contents to support large applications (e.g., e-commerce), the field of artificial intelligence (AI)-empowered persuasive video generation (AIPVG) has gained traction in recent years. This field is interdisciplinary in nature, which makes it challenging for new researchers to grasp. Currently, there is no comprehensive survey of AIPVG available. In this paper, we bridge this gap by reviewing key AI techniques that can be utilized to automatically generate persuasive videos. We offer a first-of-its-kind taxonomy which divides AIPVG into three major steps: (1) visual material understanding, which extracts information from the visual materials (VMs) relevant to the target of promotion; (2) visual storyline generation, which shortlists and arranges high-quality VMs into a sequence in order to compose a storyline with persuasive power; and (3) post-production, which involves background music generation and still image animation to enhance viewing experience. We also introduce the evaluation metrics and datasets commonly adopted in the field of AIPVG. We analyze the advantages and disadvantages of the existing works belonging to the above-mentioned steps, and discuss interesting potential future research directions.
URI: https://hdl.handle.net/10356/179066
ISSN: 0360-0300
DOI: 10.1145/3588764
Schools: College of Computing and Data Science 
School of Computer Science and Engineering 
Rights: © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1145/3588764.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CCDS Journal Articles

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