Interactive Video Generation With Real-Time Frame Adaptation
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Solution Overview
Problem
Existing video generation techniques are unable to dynamically adjust video content in real-time based on interactive operations, and they primarily produce fixed video files rather than real-time video streams, which is insufficient for scenarios requiring immediate feedback and high interactivity.
Innovation Solution
A method and system for real-time interactive video generation involving data acquisition, preprocessing, model training, and video pre-generation using a trained interactive video generation model that includes low-resolution image reconstruction and super-resolution models, enabling dynamic adjustment of video content based on interactive inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video generation techniques use pre-determined fixed frames (Scheme 1 or Scheme 2), then video content can be generated, but the video content cannot be dynamically adjusted according to real-time interactive operations
Solution Approach 1:
The patent segments the video generation process into multiple independent models: a low-resolution image reconstruction model, a super-resolution model, and an interactive video generation model. This segmentation allows each model to specialize in specific tasks, enabling real-time interactive adjustment while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent implements dynamic adaptability by designing the interactive video generation model to accept real-time interactive operations as input, allowing the video content to be dynamically adjusted frame-by-frame based on user interactions, transitioning from static pre-determined frames to dynamic interactive generation.
2Productivity
If video generation techniques produce fixed video files, then complete video content can be generated, but real-time video streams cannot be generated for scenarios requiring immediate feedback
Solution Approach 1:
The patent separates video generation into two stages: offline pre-generation of reference frames and real-time generation of intermediate frames. The pre-generated high-quality reference frames serve as templates, allowing real-time generation to focus on generating only the necessary intermediate frames through interpolation, thus achieving both high speed and maintained quality.
Solution Approach 2:
The patent performs preliminary action by pre-generating key reference frames offline using computationally intensive methods. These pre-generated frames are stored and reused during real-time interactive operations, eliminating the need to regenerate complete video sequences in real-time, thereby achieving fast real-time performance without sacrificing overall video quality.
3Adaptability or versatility
If interactive information is recorded for each video segment with high frame rate, then real-time interaction capability is improved, but data processing complexity increases
Solution Approach 1:
The patent segments interactive information processing by associating specific interactive information with specific video segments rather than processing all frames uniformly. This segmentation allows the system to handle interactive data at appropriate granularities, reducing overall processing complexity while maintaining real-time interaction capability.
Solution Approach 2:
The patent uses copying by generating multiple video segments with overlapping frames from a single pre-generated reference video. This copying approach allows the system to create multiple interactive video variants without重新 generating all content from scratch, reducing data processing complexity while maintaining interactive capabilities.
Data Source
AI summary
The present application provides a real-time interactive video generation method and system, relates to the field of deep learning technology, including acquiring training data including interactive information and video data corresponding to the interactive information; preprocessing the training data to obtain target training data; performing model training including a model pre-training process and a video pre-generation process based on the target training data, wherein 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 a pre-generation of a video based on an interactive video generation model to complete a training of the interactive video generation model, the trained interactive video generation model having the low-resolution image reconstruction model and the super-resolution model, the trainings of which have been completed; and generating an interactive video by using the trained interactive video generation model.