Feature Extraction Method for Shot Based Animation Script Creation Empowered by Artificial Intelligence

Résumé fourni par la source

In order to support precise expression and efficient creation in the animation production process, this paper proposes a feature extraction method for animation script creation by introducing deep learning algorithms from artificial intelligence. First, the basic elements of animation script creation, including screen content, shot motion and time length, were analyzed. Subsequently, in the TF-IDF algorithm, the importance of keywords in the script is quantified by calculating word frequency and inverse text frequency. In the image block sparse representation method, the sparsity degree is used to represent the number of blocks and the target state is described by extracting image features. Finally, using convolutional neural network methods, feature extraction of segmented scripts for animation script creation is achieved through steps such as constructing two-dimensional matrices, performing convolution operations, segment pooling and feature extraction. The experimental results show that the method proposed in this paper has excellent accuracy in extracting features from shot scripts in animation script creation. It can support precise expression and efficient creation in the animation production process, improve the accuracy of feature extraction and provide strong support for the visualization of animation scripts and the design of shot language.

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