Generative AI Is a Boon to the Entertainment Industry

Authors

  • Veer Tatiya Undergraduate Student, Department of Computer Science and Technology, Amity School of Engineering & Technology, Amity University, Raipur, Chhattisgarh, India
  • Sushil Jain Undergraduate Student, Department of Computer Science and Technology, Amity School of Engineering & Technology, Amity University, Raipur, Chhattisgarh, India
  • Goldi Soni Assistant Professor, Department of Computer Science and Technology, Amity School of Engineering & Technology, Amity University, Raipur, Chhattisgarh, India

Keywords:

AI-driven, Entertainment industry, Generative AI, Generative Adversarial Networks (GANs), Virtual Reality (VR), Visual Effects (VFX)

Abstract

Generative AI is transforming the entertainment industry by automating the creation of unique content across various mediums, such as film, music, gaming, and visual arts. This technology differs from traditional AI by using advanced algorithms like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Generative Pre-trained Transformers (GPT) to produce new outputs, including realistic digital characters, AI-composed music, and dynamic gaming environments. Key applications include AI-driven scriptwriting, automated visual effects (VFX), virtual actors, AI-generated compositions, and procedural content creation in gaming, as seen in examples like Disney’s The Lion King remake, AI music platforms like OpenAI’s Juke deck, and expansive worlds in No Man’s Sky. While generative AI offers benefits like reduced production time, costs, and enhanced creativity, it raises concerns over intellectual property, job displacement, and ethical issues like deepfakes, bias, privacy, and harmful stereotypes. To harness generative AI’s potential responsibly, the paper emphasizes the need for ethical and legal frameworks addressing content ownership, originality, inclusivity, and transparency. As the technology advances, generative AI promises to shape interactive storytelling, Virtual Reality (VR), and personalized content creation, pushing creative limits in the entertainment industry for both creators and audiences.

References

D. Fallis, "The epistemic threat of deepfakes," Philos. Technol., vol. 34, no. 4, pp. 623, Aug. 2020. https://doi.org/10.1007/s13347-020-00419-2

I. Goodfellow, J. Shlens, and C. Szegedy, "Generative Adversarial Networks," in Advances in Neural Information Processing Systems, 2014.

A. Elgammal, B. Liu, M. Elhoseiny, and M. Mazzone, "CAN: Creative adversarial networks, generating 'art' by learning about styles and deviating from style norms," arXiv preprint arXiv:1706.07068, Jun. 21, 2017. https://doi.org/10.48550/arXiv.1706.07068

D. Huang and F. Guo, "Multiplicity of periodic bouncing solutions for generalized impact Hamiltonian systems," Boundary Value Probl., vol. 2019, no. 1, p. 57, Mar. 2019. https://link.springer.com/article/10.1186/s13661-019-1169-1

A. Summerville, S. Snodgrass, M. Guzdial, C. Holmgård, A. K. Hoover, A. Isaksen, A. Nealen, and J. Togelius, "Procedural content generation via machine learning (PCGML)," IEEE Trans. Games, vol. 10, no. 3, pp. 257–270, Jun. 2018. https://doi.org/10.1109/TG.2018.2846639

G. N. Yannakakis and J. Togelius, Artificial Intelligence and Games. New York: Springer, Jan. 26, 2018. https://link.springer.com/book/10.1007/978-3-319-63519-4

J.-P. Briot, G. Hadjeres, and F.-D. Pachet, "Deep learning techniques for music generation A survey," arXiv preprint arXiv: 1709.01620, Sep. 5, 2017. https://doi.org/10.48550/arXiv.1709.01620

J. Buolamwini and T. Gebru, “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,” Proceedings of Machine Learning Research, vol. 81, pp. 77–91, 2018, Available: https://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf

K. LaGrandeur, “Androids and the Posthuman in Television and Film,” The Palgrave Handbook of Posthumanism in Film and Television, pp. 111–119, 2015, doi: https://doi.org/10.1057/9781137430328_12.

R. Chesney and D. Citron, "Deepfakes and the new disinformation war: The coming age of post-truth geopolitics," Foreign Affairs, vol. 98, no. 1, pp. 147+, Jan.–Feb. 2019. https://go.gale.com/ps/i.do?p=EAIM&u=anon~3fbf1989&id=GALE%7CA566263296&v=2.1&it=r&sid=sitemap&asid=585a2a7a

Vaccari and A. Chadwick, "Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news," Social Media + Society, vol. 6, no. 1, p. 2056305120903408, Feb. 2020. https://doi.org/10.1177/2056305120903408

S. Zuboff, The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. London, U.K.: Profile Books, Jan. 31, 2019.

M. Brundage, "Taking superintelligence seriously: Superintelligence: Paths, Dangers, Strategies by Nick Bostrom (Oxford University Press, 2014)," Futures, vol. 72, pp. 32–35, Sep. 1, 2015. https://doi.org/10.1016/j.futures.2015.07.009

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, "Attention is all you need," in Advances in Neural Information Processing Systems, vol. 30, 2017. 13. https://proceedings.neurips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html

D. P. Kingma and M. Welling, "Auto-Encoding Variational Bayes," arXiv preprint arXiv:1312.6114, Dec. 2013. https://doi.org/10.48550/arXiv.1312.6114

L. Floridi and M. Chiriatti, "GPT-3: Its nature, scope, limits, and consequences," Minds Mach., vol. 30, no. 4, pp. 681–694, Dec. 2020. https://link.springer.com/article/10.1007/s11023-020-09548-1

Published

2025-04-17