Neural Network Pair for Real-Time Photorealistic Video Effects
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Solution Overview
Problem
Current methods for transforming visual appearances of subjects lack the ability to generate photorealistic synthetic representations in real-time, particularly in dynamic environments, and fail to effectively incorporate user-defined visual effects into live video streams.
Innovation Solution
A computer-implemented method using a pair of neural networks, where a deconvolutional neural network generates photorealistic-imitating synthetic representations by incorporating user-defined visual effects into real-time video streams, and a convolutional neural network determines the meta-parameters of latent variables to ensure realism, allowing for dynamic transformations such as face changes, object substitutions, and social characteristic alterations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional visual transformation methods are used, then the transformation process is simple, but the output lacks photorealistic quality and cannot handle dynamic environments in real-time
Solution Approach 1:
The system is divided into two specialized neural networks: a deconvolutional neural network for generating photorealistic images and a convolutional neural network for determining meta-parameters. This segmentation allows each network to specialize in specific tasks, achieving high photorealistic quality while managing complexity through functional division.
Solution Approach 2:
The system performs preliminary training of both neural networks using training sets before real-time operation. The deconvolutional network is pre-trained to generate photorealistic images, and the convolutional network is pre-trained to determine appropriate meta-parameters. This preliminary action ensures that when the system operates in real-time, it can produce high-quality results without excessive computational complexity during execution.
2Productivity
If real-time processing is implemented, then dynamic transformations are enabled, but processing speed and computational resources are constrained
Solution Approach 1:
Both neural networks are trained in advance using comprehensive training sets containing real images and synthetic images with visual effects. This preliminary training phase performs the heavy computational work, allowing the system to operate efficiently in real-time with reduced computational resource consumption during actual processing.
Solution Approach 2:
The system introduces meta-parameters as an intermediary between the input image and the visual effect application. The convolutional neural network determines these meta-parameters, which then guide the deconvolutional network in generating the final photorealistic output. This intermediary approach simplifies the real-time processing by breaking down the complex transformation into manageable steps.
3Adaptability or versatility
If user-defined visual effects are incorporated, then customization is improved, but the system's ability to maintain realism across different effects is compromised
Solution Approach 1:
Meta-parameters serve as intermediaries that bridge user-defined visual effects and the neural network processing. The convolutional neural network analyzes the input image and determines appropriate meta-parameters that ensure the visual effects maintain realism. These meta-parameters then guide the deconvolutional network to apply the effects while preserving photorealistic quality, thus maintaining realism consistency across different customized effects.
Solution Approach 2:
The system changes parameters (meta-parameters) based on the input image characteristics and desired visual effects. By dynamically adjusting these parameters, the system can accommodate various user-defined effects while maintaining realism. The parameter changes allow the system to adapt to different effects without compromising the photorealistic quality of the output.
Data Source
AI summary
In some embodiments, an exemplary inventive computer-implemented method may include steps, performed by a processor, of: obtaining training real representations of a real subject; obtaining a training synthetic representation having a visual effect applied to a synthetic subject; training a first neural network and a second neural network by: presenting the first neural network with training real representation and candidate meta-parameters of latent variables for the visual effect to generate a training photorealistic-imitating synthetic representation of the real subject with the visual effect; presenting the second neural network with the training photorealistic-imitating synthetic representation and the training synthetic representation to determine actual meta-parameters of the latent variables of the visual effect, where the actual meta-parameters are meta-parameters at which the second neural network has identified that the training photorealistic-imitating synthetic representation is realistic, and presenting to the first neural network another real representation and the actual meta-parameters of the latent variables of the visual effect to incorporate the visual effect into another real subject.