AI-Empowered Persuasive Video Generation: A Survey
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Abstract (arXiv)
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.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Dec 2021.
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Citing papers per year: 2024: 3; 2025: 2; 2026: 5.
10 papers.
- 2024A Survey on Generative AI and LLM for Video Generation, Understanding, and Streamingcited by 37
- 2024SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learningcited by 5
- 2026BrandFusion: A Multi-Agent Framework for Seamless Brand Integration in Text-to-Video Generationcited by 3
- 2025Compliance Rating Scheme: A Data Provenance Framework for Generative AI Datasetscited by 2
- 2026Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativitycited by 2
- 2024A Universal Knowledge Model and Cognitive Architecture for Prototyping AGIcited by 1
- 2025DocReward: A Document Reward Model for Structuring and Stylizingcited by 1
- 2026"I'm happy even though it's not real": GenAI Photo Editing as a Remembering Experiencecited by 0
- 2026Ethics Testing: Proactive Identification of Generative AI System Harmscited by 0
- 2026CogenPVG: Cognitive-Enhanced Reflective Multi-Agent Framework for Persuasive Video Generationcited by 0
- 2026CogenPVG: Cognitive-Enhanced Reflective Multi-Agent Framework for Persuasive Video Generation22 Sep 2026
- 2026Ethics Testing: Proactive Identification of Generative AI System Harms23 Apr 2026
- 2026Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity3 Apr 2026
- 2026BrandFusion: A Multi-Agent Framework for Seamless Brand Integration in Text-to-Video Generation3 Mar 2026
- 2026"I'm happy even though it's not real": GenAI Photo Editing as a Remembering Experience3 Feb 2026
- 2025Compliance Rating Scheme: A Data Provenance Framework for Generative AI Datasets25 Dec 2025
- 2025DocReward: A Document Reward Model for Structuring and Stylizing13 Oct 2025
- 2024SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning15 May 2024
- 2024A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming30 Jan 2024
- 2024A Universal Knowledge Model and Cognitive Architecture for Prototyping AGI11 Jan 2024