THE ROLE OF PHYSICS ENGINES IN CGI: AI-POWERED REALISM IN ANIMATION
DOI:
https://doi.org/10.29121/shodhkosh.v5.i6.2024.4782Keywords:
Artificial Intelligence, Computer-Generated Imagery, Physics, Animation, Deep LearningAbstract [English]
The integration of physics engines and artificial intelligence (AI) has revolutionized the field of computer-generated imagery (CGI), enabling the creation of highly realistic animations. Traditional physics engines simulate real-world behaviors such as gravity, fluid dynamics, and collision detection, but they often require extensive manual fine-tuning by animators to achieve the desired realism. AI-powered techniques, particularly deep learning models and reinforcement learning, have emerged as powerful tools to optimize and enhance these simulations, leading to improved efficiency and realism. This paper explores the role of physics engines in CGI, the challenges associated with traditional approaches, and the advancements enabled by AI. We analyze existing techniques and propose an AI-driven framework that leverages deep reinforcement learning to optimize physics simulations for CGI applications. This framework aims to reduce manual intervention while achieving a more lifelike representation of real-world physics, thereby streamlining the animation production pipeline and enhancing the overall visual fidelity of animated films. Through an extensive review of literature and analysis of various AI-enhanced physics engines, we provide a comprehensive understanding of the interplay between physics, AI, and CGI.
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Copyright (c) 2024 Krunal Suthar, Yogesh Patel, Harshad Chaudhary, Mitul Patel, Shraddha Modi

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