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Revolutionizing animation: unleashing the power of artificial intelligence for cutting-edge visual effects in films

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Abstract

Integrating artificial intelligence (AI) technology with the cinema and television sectors has resulted in significant transformations in the programming and production of television shows and the emergence of a novel cohort of AI-driven media. The ubiquity of AI-enabled technology enhances film and television production quality. Conversely, there has been a notable expansion in the animation sector in recent years, characterized by a growing number of film productions annually. Finding the user’s preferred animated films within the large array of information about animated movies has emerged as a notable obstacle. This article examines the sophisticated visual effects of computer vision in animated films, focusing on using artificial intelligence and machine learning technologies. This article proposes a critical perspective on fostering the advancement of cinematic visual effects through strategic means, utilizing computer vision and machine learning technology as fundamental tools for investigating novel methodologies and frameworks for achieving visual effects. This article explores new methodologies and methods for creating visual effects in moving images, using the film industry’s digitalization, intelligent advancement, and enhancement as a starting point. This article examines the application of convolutional neural algorithms in analyzing the visual effects of the Hollywood anime film “Coco.” The study’s findings indicate that the test set’s accuracy remained relatively constant at approximately 59% even after determining the model’s parameters. This outcome significantly enhances film productions’ audiovisual quality and creative standards while fostering healthy and sustainable growth in the film industry.

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Correspondence to M. Kathiravan.

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Reddy, V.S., Kathiravan, M. & Reddy, V.L. Revolutionizing animation: unleashing the power of artificial intelligence for cutting-edge visual effects in films. Soft Comput 28, 749–763 (2024). https://doi.org/10.1007/s00500-023-09448-3

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