New Visual Expression of Anime Film Based on Artificial Intelligence and Machine Learning Technology

Abstract

With the improvement of material living standards, spiritual entertainment has become more and more important. As a more popular spiritual entertainment project, film and television entertainment is gradually receiving attention from people. However, in recent years, the film industry has developed rapidly, and the output of animation movies has also increased year by year. How to quickly and accurately find the user’s favorite movies in the huge amount of animation movie data has become an urgent problem. Based on the above background, the purpose of this article is to study the new visual expression of animation movies based on artificial intelligence and machine learning technology. This article takes the film industry’s informatization and intelligent development and upgrading as the background, uses computer vision and machine learning technology as the basis to explore new methods and new models for realizing film visual expression, and proposes relevant thinking to promote the innovative development of film visual expression from a strategic level. This article takes the Hollywood anime movie “Kung Fu Panda” as a sample and uses convolutional neural algorithms to study its new visual expression. The study found that after the parameters of the model were determined, the accuracy of the test set did not change much, all around 57%. This is of great significance for improving the audiovisual quality and creative standards of film works and promoting the healthy and sustainable development of the film industry.

Movie Scene Simulation and Rendering Based on Virtual Reality Technology

“…Ren et al [13]. used machine learning and computer vision to analyse large amounts of animated movie data.…”

The Use of Digital Media Arts in Animation Filmmaking in the Age of Digital Intelligence

“…To strengthen the human-computer interaction function of VR animation and achieve better recognition performance, depth images, and color images should be combined to preprocess information data [14]. Wan, Y. et al pointed out that the number of animated movies is also increasing while the informationization of the film industry is developing, and proposed to innovate the visual presentation of animated movies by using computer vision and machine learning technology and put forward specific ideas [15]. Kumarapu, L. et al found that single-person pose estimation has been well developed, but multi-person pose estimation still has some technical problems.…”

Fusion of the Multisource Datasets for Flood Extent Mapping Based on Ensemble Convolutional Neural Network (CNN) Model

“…The ML methods are applied in two main categories: (1) supervised method by predicting some output variable associated with each input sample and (2) unsupervised method that does not need any sample data and provides a prediction by considering input feature dataset. The ML methods are widely deployed in many applications based on different sensors and datasets such as quasidistributed smart textile [37], simultaneous assessment of magnetic field intensity [38], paddy rice seed classification [39,40], anime film visualization [41], eggplant seed classification [42], regional digital construction [43], flood mapping [44], and flood prevention [45]. Although the ML methods have provided promising results in many abovementioned applications, they suffer from lower coverage and generalization [1].…”