Short Video Production with Automated Computation Scene Composition Algorithms

In this research endeavor, a comprehensive framework has been devised for the automated generation of videos facilitated by user input.

The pivotal stages in the process of Automatic Video Generation are delineated below:

Media Dataset Construction: The foundational step involves the establishment of a diverse media repository, encompassing images, videos, and audio, serving as the underpinning for video synthesis.

Pre-Algorithmic and Pre-processing Procedures: Leveraging Natural Language Processing (NLP) and regular expressions, this phase entails the purification, formatting, and transformation of textual data to facilitate subsequent processing.

Text Segmentation Algorithm: This algorithm dissects textual content into manageable segments, such as sentences or paragraphs, aiding in the identification of crucial elements for constructing video sequences.

Entity Identification: This stage encompasses the extraction and categorization of named entities and pertinent information from textual content, guiding the process of video creation.

Query Engine Queries: The generation of context-driven queries from identified entities and relationships is conducted to retrieve suitable media resources from the dataset.

Timeline Analysis: Critical for the logical assembly of videos, this step involves determining temporal relationships between text segments and media objects.

Text and Media Integration: In the final phase, the amalgamation of text and media is executed using video editing software APIs or customized rendering engines, culminating in the production of the final video.

Consequently, this methodological approach has facilitated the establishment of a public repository."

本研究构建了一套面向用户输入驱动的视频自动生成综合框架。

自动视频生成流程的关键环节详述如下：

媒体数据集构建（Media Dataset Construction）：作为核心奠基环节，该步骤需搭建涵盖图像、视频与音频的多元化媒体资源库，为视频合成提供底层支撑。

算法预处理流程（Pre-Algorithmic and Pre-processing Procedures）：本阶段借助自然语言处理（Natural Language Processing, NLP）与正则表达式，对文本数据进行净化、格式化与转换处理，以适配后续流程。

文本分段算法（Text Segmentation Algorithm）：该算法将文本内容拆解为句子、段落等可管理的分段单元，助力识别构建视频序列所需的核心要素。

实体识别（Entity Identification）：此环节需从文本内容中提取并分类命名实体与相关信息，为视频创作流程提供指引。

查询引擎查询（Query Engine Queries）：基于已识别的实体与关联关系生成上下文驱动的查询语句，以从数据集内检索适配的媒体资源。

时间线分析（Timeline Analysis）：该步骤对视频的逻辑编排至关重要，需确定文本分段与媒体对象间的时序关联关系。

文本与媒体融合（Text and Media Integration）：作为最终环节，本步骤通过视频编辑软件应用程序接口（API）或定制化渲染引擎，实现文本与媒体的融合，最终生成目标视频成品。

综上，本研究方法已助力建成一处公开资源库。