Comprehensive Investigation of Algorithmic Models and Prospects in Artificial Intelligence Generated Content
DOI:
https://doi.org/10.61173/4z7x8q33Keywords:
Artificial intelligence generated content, variational automatic encoders, diffusion model, large language modelAbstract
Artificial Intelligence Generated Content (AIGC) has quickly evolved into a critical paradigm for automated content creation, supplementing traditional professionally and user-generated content. Empowered by deep learning, AIGC enables the generation of excellent text, pictures, audio, and video across diverse domains. This paper provides an exhaustive investigation of the core algorithmic models that underpin AIGC technologies, including Generative opposite Networks, Variational Automatic Encoders, Diffusion Models, and Large Language Models. This paper analyzes their principles, technical advancements, and representative applications in visual, textual, and speech generation. Furthermore, the current limitations related to controllability, computational overhead, domain generalization, and ethical considerations were discussed. Looking forward, this paper highlights emerging trends and research directions aimed at improving interpretability, efficiency, and trustworthiness in AIGC systems. By combining technical insight with application-oriented discussion, this paper aims to provide a comprehensive foundation for future research and guide the safe, effective and large-scale deployment of generative artificial intelligence across industries.
References
[1] Hao Y, Liu Y, Mou L. Teacher forcing recovers reward functions for text generation. Advances in Neural Information Processing Systems (NeurIPS), 2022, 35: 12594–12607.
[2] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks. Communications of the ACM, 2020, 63(11): 139–144.
[3] Croitoru FA, Hondru V, Ionescu RT, et al. Diffusion models in vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(9): 10850–10869.
[4] Doersch C. Tutorial on variational autoencoders. arXiv Preprint, 2016, arXiv:1606.05908.
[5] Wan T, Wang A, Ai B, et al. WAN: Open and advanced large-scale video generative models. arXiv Preprint, 2025, arXiv:2503.20314.
[6] Rumelhart DE, Hinton GE, Williams RJ. Learning representations by backpropagating errors. Nature, 1986, 323(6088): 533–536.
[7] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Advances in Neural Information Processing Systems (NeurIPS), 2014, 27.
[8] Deng K, Yang G, Ramanan D, et al. 3D-aware conditional image synthesis. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023: 4434– 4445.
[9] Yin F, Zhang Y, Wang X, et al. 3D GAN inversion with facial symmetry prior. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023: 342– 351.
[10] Xu X, Navasardyan S, Tadevosyan V, et al. Image completion with heterogeneously filtered spectral hints. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023: 4591–4601.
[11] Jain J, Zhou Y, Yu N, et al. Keys to better image inpainting: Structure and texture go hand in hand. Proceedings of the IEEE/ CVF Winter Conference on Applications of Computer Vision (WACV), 2023: 208–217.
[12] Kanagawa H, Ijima Y. Enhancement of text-predicting style token with Generative Adversarial Network for expressive speech synthesis. ICASSP 2023 – IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2023: 1–5.
[13] Melechovsky J, Mehrish A, Sisman B, et al. Accent conversion in text-to-speech using multi-level VAE and adversarial training. arXiv Preprint, 2024, arXiv:2406.01018.
[14] Qu Y, Tan Q, Xie H, et al. Exploring stroke-level modifications for scene text editing. Proceedings of the AAAI Conference on Artificial Intelligence, 2023, 37(2): 2119–2127.
[15] Yoneyama R, Wu YC, Toda T. Source-filter HiFi-GAN: Fast and pitch controllable high-fidelity neural vocoder. ICASSP 2023 – IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2023: 1–5.
[16] Bai Y, Geng X, Mangalam K, et al. Sequential modeling enables scalable learning for large vision models. arXiv Preprint, 2023, arXiv:2312.00785.
[17] Radford A, Narasimhan K, Salimans T, et al. Improving language understanding by generative pre-training. OpenAI Blog, 2018.
[18] Liu A, Feng B, Xue B, et al. DeepSeek-V3 technical report. arXiv Preprint, 2024, arXiv:2412.19437.
[19] Ramesh A, Dhariwal P, Nichol A, et al. Hierarchical textconditional image generation with CLIP latents. arXiv Preprint, 2022, 1(2): 3.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
