Special Effect Video Generation Using Generative Adversarial Networks
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Solution Overview
Problem
Current short video apps lack effective methods to generate special effect videos that enhance user experience and make videos more interesting, as they rely on limited editing options and lack advanced algorithms for dynamic special effect integration.
Innovation Solution
A special effect video generation method and apparatus that acquires person portrait images and special effect information sequences, using a trained generative adversarial network model to produce and stitch special effect images into a cohesive video, improving aesthetics and authenticity through sequential processing and model training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional video editing methods are used in short video apps, then the app structure remains simple and easy to operate, but the video content becomes boring and user experience deteriorates
Solution Approach 1:
The system enables automatic special effect video generation where the model autonomously processes portrait images and special effect information sequences to produce special effect videos without requiring manual user intervention for complex editing operations
Solution Approach 2:
Traditional manual video editing operations are replaced by an automated special effect generation model that takes portrait images and special effect parameters as input and generates complete special effect videos programmatically
2Productivity
If advanced special effect generation models are deployed on mobile terminals, then user experience improves through high-quality special effect videos, but device complexity and computational resource requirements increase
Solution Approach 1:
The system separates the special effect generation model deployment into different tiers: complex models can be deployed on servers for high-quality generation, while simplified versions or pre-processed results can be used on mobile terminals, allowing quality optimization without forcing full model deployment on resource-constrained devices
Solution Approach 2:
The system uses portrait images and special effect information sequences as intermediary representations that can be processed by the generation model. These structured inputs and outputs enable the model to operate efficiently on mobile devices by working with compact data representations rather than raw video data
Data Source
AI summary
Embodiments of the present disclosure disclose a special effect video generation method and apparatus, a device, and a storage medium. One person portrait image or a plurality of person portrait images are acquired, and a special effect information sequence is obtained. The one person portrait image and the special effect information sequence are input into a first special effect generation model, or the plurality of person portrait images and the special effect information sequence are input into the first special effect generation model, to obtain a plurality of special effect images. The plurality of special effect images are stitched in the set order, to obtain a target special effect video.