AI-Generated Content (AIGC): A Survey
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Abstract (arXiv)
To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace manual content generation by generating content based on user-inputted keywords or requirements. The development of large model algorithms has significantly strengthened the capabilities of AIGC, which makes AIGC products a promising generative tool and adds convenience to our lives. As an upstream technology, AIGC has unlimited potential to support different downstream applications. It is important to analyze AIGC's current capabilities and shortcomings to understand how it can be best utilized in future applications. Therefore, this paper provides an extensive overview of AIGC, covering its definition, essential conditions, cutting-edge capabilities, and advanced features. Moreover, it discusses the benefits of large-scale pre-trained models and the industrial chain of AIGC. Furthermore, the article explores the distinctions between auxiliary generation and automatic generation within AIGC, providing examples of text generation. The paper also examines the potential integration of AIGC with the Metaverse. Lastly, the article highlights existing issues and suggests some future directions for application.
Key facts. Cited by 59 papers in the map (data through 14 Oct 2026). Builds on 40 of its 112 references that are in the map. First posted on arXiv 26 Mar 2023.
Builds on
40 of 112 references.
References · 40
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Citing papers per year: 2023: 21; 2024: 24; 2025: 13; 2026: 1.
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- 2025ExtremeAIGC: Benchmarking LMM Vulnerability to AI-Generated Extremist Content13 Mar 2025
- 2025EigenActor: Variant Body-Object Interaction Generation Evolved from Invariant Action Basis Reasoning1 Mar 2025
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- 2023DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated Contentcited by 0
- 2025ExtremeAIGC: Benchmarking LMM Vulnerability to AI-Generated Extremist Content13 Mar 2025
- 2024AIGCs Confuse AI Too: Investigating and Explaining Synthetic Image-induced Hallucinations in Large Vision-Language Models13 Mar 2024
- 2023DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated Content16 Dec 2023
- 2023Multimodal Large Language Models: A Survey22 Nov 2023
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- 2025Bridging Text and Video Generation: A Survey6 Oct 2025
- 2025FlashDP: Private Training Large Language Models with Efficient DP-SGD1 Jul 2025
- 2025Towards Generalized Proactive Defense against Face Swapping with Contour-Hybrid Watermark25 May 2025
- 2025The Effects of Grouped Structural Global Pruning of Vision Transformers on Domain Generalisation5 Apr 2025
- 2025Strategic Prompt Pricing for AIGC Services: A User-Centric Approach23 Mar 2025
- 2025EigenActor: Variant Body-Object Interaction Generation Evolved from Invariant Action Basis Reasoning1 Mar 2025
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