Virtual Audience Generation Using GANs for Live Events
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Solution Overview
Problem
Current solutions for generating virtual audiences in live events lack visual fidelity and fail to incorporate auditory reactions, often resulting in delayed user responses, which diminish the emotional impact and home team advantage in events like sports and music performances.
Innovation Solution
A computer-implemented method using a generative adversarial network (GAN) to dynamically generate virtual audience members based on user preferences and event occurrences, incorporating real-time user reactions and contextual awareness to create immersive experiences by stitching audience reactions to event moments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If current solutions for generating virtual audiences are used, then the implementation is simple, but the visual fidelity and emotional impact are insufficient
Solution Approach 1:
The patent uses Generative Adversarial Networks (GANs) to create photorealistic copies of real audience members. The generator network learns to synthesize virtual audience images that visually indistinguishable from real photographs, while the discriminator network evaluates authenticity. This copying approach achieves high visual fidelity by replicating the appearance, expressions, and characteristics of real audience members rather than using simple graphical representations.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based audience generation systems with machine learning-based neural networks. Instead of using pre-defined graphical audience models or simple animation techniques, the system employs GANs to automatically generate photorealistic virtual audience members, substituting complex computational mechanisms for simpler visual output.
2Reliability
If real-time audience reactions are incorporated, then the emotional impact is enhanced, but the response time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and analyzing audience reaction data in advance of actual event occurrences. The GAN model is pre-trained on extensive audience reaction datasets, enabling it to quickly generate appropriate virtual audience responses when triggered by event moments. This preliminary preparation reduces the computational time required during live events while maintaining emotional authenticity.
Solution Approach 2:
The patent implements feedback mechanisms where the discriminator network continuously evaluates the authenticity of generated virtual audience reactions. This feedback loop ensures that the emotional responses generated by the generator network maintain high levels of authenticity and realism, allowing the system to adjust and refine its output in real-time to preserve emotional impact.
3Manufacturing precision
If virtual audience members are generated with high visual fidelity, then the immersive experience is improved, but the computational resources required increase
Solution Approach 1:
The system performs computationally intensive GAN training and model generation in advance, before the actual live events occur. By pre-processing the heavy computational work of training the generator and discriminator networks on extensive datasets, the system stores the learned patterns and knowledge in the trained model. During live events, only lightweight inference operations are required to generate virtual audience members, dramatically reducing real-time computational energy consumption while maintaining high visual realism.
Data Source
AI summary
One or more computer processors create a user-event localization model for an identified remote audience member in a plurality of identified remote audience members for an event. The one or more computer processors generate a virtual audience member based the identified remote audience member utilizing a trained generated adversarial network and one or more user preferences. The one or more computer processors present the generated virtual audience member in a location associated with the event. The one or more computer processors dynamically adjust a presented virtual audience member responsive to one or more event occurrences utilizing the created user-event localization model.