Interactive Video Generation With Low-Res Preprocessing for Real-Time Output
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Solution Overview
Problem
Existing video generation techniques are unable to dynamically adjust frames based on real-time interactive operations, and they primarily produce fixed video content that cannot be interactively modified, lacking the capability to generate real-time video streams.
Innovation Solution
A method and system for generating real-time target video involves acquiring training data, preprocessing it to obtain low-resolution images, and performing model training using a low-resolution image reconstruction model and a super-resolution model to create an interactive video generation model that can produce dynamic video content based on interactive inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video generation is performed using traditional neural network methods or 3D generation models, then video content can be generated, but the video content is fixed and cannot be dynamically adjusted according to real-time interactive operations
Solution Approach 1:
The video generation model is segmented into three independent modules: a low-resolution video generation module, a low-resolution to high-resolution translation module, and a frame interpolation module. Each module has a specific function and can be independently trained and optimized, allowing the system to handle interactive inputs dynamically while maintaining manageable model complexity
Solution Approach 2:
The system transitions from static pre-generated video content to dynamic real-time video generation by implementing a multi-stage processing pipeline that can respond to interactive operations. The low-resolution generation module produces intermediate frames that are then enhanced and interpolated in real-time based on user interactions
2Speed
If video generation is performed using existing techniques, then video files can be produced, but real-time video stream generation with immediate feedback is not achieved
Solution Approach 1:
The video generation process is divided into separate stages: low-resolution generation, super-resolution translation, and frame interpolation. This segmentation allows each stage to be optimized for its specific task, enabling real-time processing speed while maintaining high output quality through specialized processing at each stage
Solution Approach 2:
The system performs preliminary generation of low-resolution video frames first, which are then enhanced through super-resolution translation and frame interpolation. This preliminary action approach allows for faster initial generation that can be refined in real-time, achieving both speed and quality requirements
3Measurement precision
If high-resolution video is generated directly, then video quality is maintained, but processing time increases and real-time interaction is compromised
Solution Approach 1:
The system separates the video generation process into low-resolution generation and high-resolution translation stages. The low-resolution generation module produces frames quickly, while the translation module enhances resolution in real-time, avoiding the computational burden of generating high-resolution frames directly
Solution Approach 2:
Low-resolution video frames serve as an intermediary between the fast generation process and the high-quality output requirement. These intermediate frames are processed through super-resolution translation and frame interpolation to achieve high resolution without the time cost of direct high-resolution generation
Data Source
AI summary
A method and a system for generating real-time target video are provided, the method includes: acquiring training data including interactive information and video data corresponding to the interactive information; preprocessing the training data to obtain target training data; where the preprocessing includes: performing down-sampling processing on the video data to obtain a plurality of frames of low-resolution images; performing model training to a preset model based on the target training data; the model training includes a model pre-training process and a video pre-generation process, the model pre-training process includes a training of a low-resolution image reconstruction model and a training of a super-resolution model, and the video pre-generation process includes: performing video pre-generation based on the target training data and an interactive video generation model to complete training to the interactive video generation model; and, obtaining an interactive target video through the trained interactive video generation model and input data.