Text-Driven Human Mesh Stylization for Temporally Consistent 4D Avatars
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Solution Overview
Problem
Existing methods for creating animatable and detailed 3D avatars are labor-intensive, time-consuming, and cost-inefficient, and struggle with generating temporally-consistent and detailed geometries and textures for human meshes.
Innovation Solution
A text-driven motion recommendation and neural mesh stylization system that utilizes hierarchical multi-modal motion search and decoupled neural style fields to generate human meshes with realistic geometry and texture from natural language prompts, applying style attributes like color and displacement to create 4D human avatars.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual generation methods are used to create 3D avatars, then detailed geometry and texture can be achieved, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical design processes with an automated neural network-based system. The neural mesh stylization network automatically generates detailed geometry and texture from motion sequences, substituting the manual mechanical workflow with an intelligent automated system that maintains high detail while dramatically reducing time investment.
Solution Approach 2:
The system enables self-service automation where the neural network independently performs the complex tasks of geometry generation, texture synthesis, and style transfer without human intervention. The automated pipeline processes motion data and generates complete 3D avatar sequences autonomously, eliminating the need for manual design operations.
2Loss of time
If automated animating processes are introduced, then time consumption is reduced, but generating temporally-consistent and detailed geometries and textures becomes more challenging
Solution Approach 1:
The patent applies preliminary action by first extracting and analyzing motion sequences before generating geometry and texture. The system pre-processes motion data to identify key temporal patterns and characteristics, then uses this information to guide the neural network in generating temporally-consistent results. This preliminary analysis ensures that temporal relationships are preserved throughout the automated generation process.
Solution Approach 2:
The system incorporates feedback mechanisms where the neural network evaluates generated frames against temporal consistency criteria and adjusts subsequent generations accordingly. The feedback loop ensures that geometry and texture variations across frames maintain temporal coherence, automatically correcting inconsistencies that arise during automated generation.
3Productivity
If detailed geometry and texture are generated automatically, then creation efficiency improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex avatar generation task into distinct modular components: motion sequence extraction, neural mesh stylization, geometry generation, and texture synthesis. Each module handles a specific aspect of the process, making the overall system more manageable and easier to implement while maintaining high productivity through automated processing of each segment.
Data Source
AI summary
The present disclosure provides a text-driven motion recommendation and neural mesh stylization system and a method producing human mesh animation using the same. The system comprises at least one instruction stored in a memory, and a processor that executes the at least one instruction, wherein the at least one instruction, when executed by the processor, causes the processor to find raw action labels matching a query given as a text prompt in a human motion dataset stored in a database, encode the raw action labels and the query for vectorizing the raw action labels and the query, and measure similarity between the raw action labels and the query based on the vectorized vectors.