Conditioned Virtual Representatives for Low-Latency XR Participation
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Solution Overview
Problem
Existing systems face challenges in efficiently generating high-quality virtual avatars for multi-user experiences in extended reality environments, particularly in maintaining realistic representation and low-latency rendering, especially when individuals cannot personally attend gatherings.
Innovation Solution
A virtual representative conditioning system that utilizes a conditioning engine to generate conditioned models for virtual representatives using baseline models and conditioning inputs, allowing for personalized and efficient participation in multi-user experiences by selecting and refining virtual avatars based on knowledge bases and user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If virtual avatars are generated in real-time for multi-user experiences, then user participation flexibility is improved, but rendering latency increases
Solution Approach 1:
The system pre-generates and stores baseline virtual representative models before they are needed in multi-user experiences. These baseline models are created in advance using training data and stored for rapid retrieval and conditioning, eliminating the need to generate them from scratch during real-time interactions.
Solution Approach 2:
The model generation process is divided into distinct stages: baseline model generation (performed in advance), conditioning (performed quickly at runtime using pre-extracted features), and rendering. This segmentation allows computationally intensive operations to be performed beforehand while keeping real-time operations lightweight.
2Manufacturing precision
If high-quality virtual avatar representation is maintained, then user experience quality is improved, but computational resources increase
Solution Approach 1:
Complex model training and baseline generation are performed in advance when computational resources are abundant, producing pre-trained models that can be rapidly instantiated and conditioned with minimal real-time computation while maintaining high quality representation.
Solution Approach 2:
Instead of generating new high-quality models from scratch during each interaction, the system creates copies of pre-trained baseline models and applies conditioning to them. This copying approach maintains quality while significantly reducing real-time computational burden.
3Manufacturing precision
If personalized virtual representatives are created for each user, then representation realism is improved, but model generation complexity increases
Solution Approach 1:
The personalization process is segmented into offline training (creating baseline models with user-specific characteristics) and online conditioning (applying specific experience parameters). This separation reduces real-time complexity while maintaining personalized realism through the pre-computed baseline models.
Data Source
AI summary
Systems and techniques are provided for conditioning virtual representatives. For example, a method can include obtaining, by a conditioning engine, a baseline model for a virtual representative; obtaining, by the conditioning engine, one or more conditioning inputs configured to condition an action in one or more multi-user experiences of the virtual representative; generating, based on the baseline model and the one or more conditioning inputs configured to condition an action in one or more multi-user experiences of the virtual representative, a conditioned model for the virtual representative; and outputting the conditioned model for the virtual representative.