Revolutionizing CGI and VFX with AI - Advancing Neural Rendering, Procedural Animation and Generation in Filmmaking
Keywords:
Artificial intelligence, Computer-generated imagery (CGI), Generative Adversarial networks (GANs), Motion capture, Neural Radiance Fields (NeRFs), Procedural content generation, Ray tracing, Visual effects (VFX)Abstract
This paper examines the far-reaching influence of Artificial Intelligence (AI) on Visual Effects (VFX) and Computer-Generated Imagery (CGI), with particular attention to realism, production efficiency, and the creative latitude afforded to filmmakers. AI-driven approaches — spanning Generative Adversarial Networks (GANs), Neural Radiance Fields (NeRFs), and procedural content generation — make it possible to construct photorealistic digital worlds and hyper-detailed virtual characters while keeping computational expenditure in check. Real-time rendering pipelines, strengthened by ray tracing and AI-powered denoising, allow instant on-set feedback, compressing post-production schedules. Suitless motion capture, physics-based animation, and automated compositing further liberate artists from repetitive technical tasks. By examining these technologies through both a theoretical and case-study lens, this review reveals how AI is repositioning itself not merely as a productivity tool but as a genuine creative collaborator in the filmmaking process. Persisting challenges — deepfake misuse, workforce displacement, and the black-box nature of certain neural models — are discussed alongside a forward-looking perspective on responsible AI integration.
References
N. Anantrasirichai and D. Bull, “Artificial intelligence in the creative industries: A review,” Artificial Intelligence Review, vol. 55, no. 1, pp. 589–656, Jan. 2022.
A. Tewari, O. Fried, J. Thies, V. Sitzmann, S. Lombardi, K. Sunkavalli, R. Martin-Brualla, T. Simon, J. Saragih, M. Nießner, and R. Pandey, “State of the art on neural rendering,” Computer Graphics Forum, vol. 39, no. 2, pp. 701–727, May 2020.
P. S. Chow, “Ghost in the (Hollywood) machine: Emergent applications of artificial intelligence in the film industry,” NECSUS: European Journal of Media Studies, vol. 9, no. 1, pp. 193–214, 2020.
N. Rathore, D. K. Debasis, and M. P. Singh, “Selection of optimal renewable energy resources using TOPSIS-Z methodology,” In Proceedings of the International Conference on Advanced Communication and Computational Technology, Singapore: Springer Nature Singapore, 2019, pp. 967–977.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “NeRF: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM, vol. 65, no. 1, pp. 99–106, Jan. 2022.
M. Dhipa, N. Rathore, P. P. Adivarekar, and S. T. Siddiqui, “Enhancing energy efficiency in sensor/ad-hoc networks through dynamic sleep scheduling,” ICTACT Journal on Communication Technology, vol. 14, no. 3, Sep. 2023.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” In Advances in Neural Information Processing Systems, vol. 27, 2014.
A. Pavlov, “How AI will change visual effects forever,” Videomaker, Mar. 27, 2023.
N. Nishant, N. Rathore, V. K. Nassa, V. K. Dwivedi, and S. P. Dillibabu, “Integrating machine learning and mathematical programming for efficient optimization of electric discharge machining technique,” The Scientific Temper, vol. 14, no. 3, pp. 859–863, 2023.
D. Smith, “‘Of course it’s disturbing’: Will AI change Hollywood forever?” The Guardian, Mar. 23, 2023.
N. Rathore, P. B. Acharjee, K. Thivyabrabha, and A. Ingle, “Researching brain-computer interfaces for enhancing communication and control in neurological disorders,” The Scientific Temper, vol. 14, no. 4, pp. 1098–1105, Dec. 2023.
N. Rathore, G. Soni, B. Khandelwal, R. Kashyap, B. P. Kasaraneni, and R. Nair, “Leveraging AI and blockchain for scalable and secure data exchange in IoMT healthcare ecosystems,” In 2025 4th OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 5.0 (OTCON), Apr. 2025, pp. 1–6.
J. T. Kajiya, “The rendering equation,” In Proceedings of the 13th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH ’86), Aug. 1986, pp. 143–150.
T. Müller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,” ACM Transactions on Graphics, vol. 41, no. 4, pp. 1–15, Jul. 2022.
N. Rathore and M. P. Singh, “Selection of optimal renewable energy resources in uncertain environment using ARAS-Z methodology,” In 2019 International Conference on Communication and Electronics Systems (ICCES), Jul. 2019, pp. 373–377.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2019, pp. 4401–4410.
