Text-Guided Video Editing With User-Filled Vacant Clips
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Solution Overview
Problem
Existing video production methods fail to meet users' personalized video production needs due to the inability to accurately match image materials with user input text using intelligent matching algorithms.
Innovation Solution
Generate first video editing data based on input text, allowing users to freely select image materials from the editing data, and fill vacant video clips with matching audio to create personalized videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If intelligent matching algorithm is used to automatically match input text with video images, then video production efficiency is improved, but the ability to meet personalized user needs deteriorates
Solution Approach 1:
The patent segments the video production process into distinct components: text-to-audio conversion, image material selection, and video assembly. By dividing the automated process into manageable segments, users can intervene at specific stages (particularly image selection) to personalize their videos while maintaining efficiency in other automated segments.
Solution Approach 2:
The system performs preliminary actions by pre-converting input text to audio clips and pre-assembling video structures before final rendering. This allows users to review and customize intermediate results (audio-visual alignment, image selection) before commitment, balancing automation with personalization.
2Ease of operation
If automated intelligent matching is used to generate videos from input text, then operation simplicity is improved, but user control over video content deteriorates
Solution Approach 1:
The patent introduces an intermediary editing interface between the automated text-to-video conversion and the final output. This intermediary layer allows users to review, modify, and personalize video content without complicating the initial simple operation of text input, thus maintaining ease of operation while enhancing user control.
3Device complexity
If intelligent matching algorithm automatically selects video images, then device complexity is reduced, but manufacturing precision of video content deteriorates
Solution Approach 1:
The system employs self-service mechanisms where the intelligent matching algorithm automatically performs text-to-audio conversion and initial video assembly with sufficient precision for most use cases. For users requiring higher precision, the system enables easy manual intervention without increasing overall system complexity, allowing each user to access the level of precision they need.
Data Source
AI summary
The present disclosure provides a video generation method, an apparatus, a device, a storage medium, and a program product, and the method includes: in response to a first instruction triggered for an input text, generating first video editing data based on the input text, in which the first video editing data includes a first video clip and an audio clip, a first target video clip among the first video clip is a vacant clip; displaying the first video clip and the audio clip on a video editing track of a video editor; in response to triggering a second instruction for the target video clip on the video editor, filling the first target video clip with a target video to obtain second video editing data; generating a first video based on the second video editing data.