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 a 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.
翻译:宣传视频正在迅速成为一种受欢迎的媒介,用以说服人们改变在许多场合的行为(如在线购物、社会企业倡议等)。今天,这些视频往往由专业人员制作,这是一个时间、劳力和成本密集型的工作。为了制作这类内容,以支持大型应用(如电子商务),人工智能(AI)增强的有说服力的视频制作领域近年来已获得引力。这个领域是跨学科的,使得新的研究人员难以掌握。目前,还没有对AIPVG进行全面的调查。我们通过审查可自动用于制作具有说服力的视频的主要AI技术来弥补这一差距。我们提供了一种将AIPVG分为三大主要步骤的首创性分类学:1)视觉材料理解,从与促进目标相关的视觉材料(VMS)中提取信息;2)视觉故事制作,这种短名单和安排高品质的VMS进入一个序列,以便仍然用具有说服力的力量来构建一个故事线;以及3)之后,我们通过审查关键AI技术技术技术技术,可以用来自动生成具有说服力的视频视频。我们提供了一种首创性分类,将AIPV的精准性分类,然后,从我们开始研究,从历史背景和图像实地研究。