Video material clip automatic splicing method and system based on large model

By segmenting and evaluating the importance of video footage and generating narrative text using a large model, the problems of excessive human intervention and poor automated editing in existing technologies are solved, and the automated generation of video clips that meet the user's intentions is achieved.

CN122120534BActive Publication Date: 2026-07-07NANJING NORMAL UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NANJING NORMAL UNIVERSITY
Filing Date
2026-04-27
Publication Date
2026-07-07

AI Technical Summary

Technical Problem

Existing video editing methods require a lot of manual intervention, making it difficult to automatically select and generate finished videos that meet user needs from massive amounts of video footage while ensuring narrative coherence and alignment with intent.

Method used

By dividing video footage into video segments, extracting thematic text using a graph-to-text model, and then using a text-to-text model to fuse these texts to generate narrative text, the importance of thematic texts is calculated, the importance assessment is dynamically adjusted, common narrative features are extracted, and finally the video segments are spliced ​​together according to the narrative logic.

Benefits of technology

It enables the automatic selection of key segments from massive amounts of video footage to generate finished videos that meet user needs, ensuring narrative coherence and alignment with intent.

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Abstract

The present application relates to the field of video content generation, in particular to a video material clip automatic splicing method and system based on a large model, comprising: extracting the theme text of each segment in the video material; generating a first narrative text by fusing all theme texts; calculating the importance of each theme text therein; recording the theme text with the smallest importance as a class one text, and the rest as a class two text, and increasing the importance of the class one text based on the difference between the second narrative text generated based on the class two text and the first narrative text; extracting the common narrative features of all second narrative texts after repeating the process; dividing the theme texts into two categories according to the importance, so that the third narrative text fused by the first class theme text has the maximum similarity with the common narrative features; and splicing the corresponding video segments in the order of the first class theme text in the third narrative text. The present application automatically selects key segments from massive video materials and generates a video film that meets the user's demand.
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