Avatar Semantic Segmentation for Virtual Environment Compatibility

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Solution Overview

Problem

Existing technologies face challenges in efficiently generating and processing avatars for virtual environments, particularly in detecting and optimizing semantic segments, generating hierarchical skeletons, and ensuring compatibility with virtual environments, which can lead to resource inefficiencies and suboptimal user experiences.

Innovation Solution

A computing system utilizes machine-learning models to detect segment errors in avatar assets, generate optimized semantic segments, hierarchical skeletons, and deformable mesh models, and ensure compatibility with virtual environments by adjusting mesh and texture resolutions and generating compatible avatars.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional methods are used to generate and process avatar assets, then the process is simpler, but the resource efficiency is poor and processing速度慢

Engineering Contradiction:

Improveavatar generation efficiencyVSAvoidresource usage

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The patent segments avatar assets into semantic segments (e.g., head, body, limbs) and processes them independently through machine learning models. This segmentation enables parallel processing and optimization of individual segments, improving overall generation efficiency while reducing redundant computations and resource consumption.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent replaces traditional manual or rule-based avatar generation methods with machine learning models. These models automatically learn optimal avatar configurations from data, substituting mechanical processing with intelligent algorithms that improve efficiency and reduce resource usage through smarter computation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If traditional semantic segment generation is used, then the process is faster, but segment errors are not detected and optimization is suboptimal

Engineering Contradiction:

Improvesemantic segment accuracyVSAvoidprocessing speed

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The patent implements a feedback mechanism where machine learning models generate semantic segments, detect errors in these segments, and then optimize them iteratively. The error detection results feed back into the generation process, allowing continuous improvement of segment accuracy while maintaining processing efficiency through automated correction.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent performs preliminary error detection and optimization on semantic segments before final avatar assembly. By identifying and correcting segment errors early in the process, the system prevents propagation of errors to later stages, improving overall accuracy without requiring extensive rework and maintaining processing speed.

Inventive Principle:

Principle #10Preliminary action

3Manufacturing precision

If high-resolution meshes and textures are used, then the avatar quality is better, but the compatibility with virtual environments may be compromised

Engineering Contradiction:

Improveavatar qualityVSAvoidvirtual environment compatibility

Core Design Contradiction:

Manufacturing precisionVSAdaptability or versatility

Solution Approach 1:

The patent dynamically adjusts mesh and texture resolution parameters based on the target virtual environment's requirements. The system analyzes environment specifications and automatically optimizes asset parameters to achieve the best possible quality within compatibility constraints, rather than using fixed high-resolution settings that may not suit all environments.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent applies different quality levels to different parts of the avatar based on their importance and the environment's requirements. Critical areas receive higher resolution while less important areas use lower resolution, optimizing the balance between overall avatar quality and compatibility with various virtual environments.

Inventive Principle:

Principle #3Local quality

4Productivity

If manual avatar processing is used, then the control is more precise, but the resource efficiency is poor

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent implements automated systems where machine learning models perform avatar generation, error detection, and optimization tasks without manual intervention. The system serves itself by automatically adjusting parameters, detecting errors, and generating optimized assets, dramatically improving processing efficiency while the modular architecture manages complexity through automation rather than manual control mechanisms.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250336165A1Generation and Processing of Avatars

Publication Date: 2025.10.30 GENIES INC

AI summary

Provided are methods, systems, devices, apparatuses, and tangible non-transitory computer readable media for processing avatar content that can be used in a virtual environment. The disclosed technology can generate optimized semantic segments based on assets comprising meshes and textures associated with avatars. Further, the disclosed technology can generate hierarchical skeletons, deformable mesh models, and facial expressions on facial regions of the mesh models. Further, the compatibility of avatars with a virtual environment can be determined and compatible avatars and granular assets associated with avatars can be sent to remote computing systems that are configured to implement the avatars.