3D Human Model Reconstruction by Facial-Body Splicing
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Solution Overview
Problem
Current virtual image generation technologies are limited in reconstructing full-body models, as existing methods focus primarily on facial modeling and lack flexibility in integrating facial features with body models, making it difficult to interact with conventional engines and integrate with three-dimensional art production.
Innovation Solution
A method involving acquiring a human body image, generating a three-dimensional facial model from a partial image, preprocessing it to enhance flexibility, and splicing it with a three-dimensional body model to create a complete human body model, utilizing neural networks and preprocessing techniques to improve accuracy and compatibility with 3D engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a three-dimensional facial model is generated from a two-dimensional image, then facial feature representation is improved, but model accuracy and realism deteriorate due to loss of depth and spatial information
Solution Approach 1:
The patent transforms a two-dimensional facial image into a three-dimensional facial model by adding depth and spatial dimensions. This dimensionality change enables the model to represent facial features with proper spatial relationships, resolving the contradiction between feature representation and model accuracy.
Solution Approach 2:
The patent introduces intermediate processing steps including facial feature point detection, 3D coordinate calculation, and mesh generation as mediators between the 2D input image and the final 3D model. These intermediaries preserve spatial information that would otherwise be lost in direct transformation.
2Adaptability or versatility
If a complete three-dimensional human body model is created by splicing facial model and body model, then full-body representation is improved, but model integration complexity deteriorates
Solution Approach 1:
The patent divides the complete human body model into separate segments: a three-dimensional facial model and a three-dimensional body model. Each segment is processed and optimized independently, then spliced together to form the complete model. This segmentation reduces integration complexity while maintaining full-body representation.
Solution Approach 2:
The patent merges the separately processed facial model and body model into a unified three-dimensional human body model. The splicing operation combines the two segments with proper coordinate alignment and mesh continuity, achieving complete full-body representation without excessive integration complexity.
3Adaptability or versatility
If preprocessing is applied to enhance facial model flexibility, then model adaptability is improved, but processing time and computational complexity deteriorate
Solution Approach 1:
The patent performs preprocessing operations on the three-dimensional facial model before final integration with the body model. This preliminary action includes optimizing mesh structure, calculating normal vectors, and preparing texture coordinates in advance. By performing these operations beforehand, the model gains flexibility for subsequent operations while managing computational complexity efficiently.
Data Source
AI summary
Disclosed are a model reconstruction method, a model processing method and apparatus, a device, a system, and a medium. The model reconstruction method includes: acquiring a human body image; generating a first three-dimensional facial model based on a first partial image in the human body image and generating a three-dimensional body model, wherein the first partial image includes facial features; preprocessing the first three-dimensional facial model and obtaining a second three-dimensional facial model based on a preprocessing result; and splicing the second three-dimensional facial model and the three-dimensional body model to obtain a spliced three-dimensional human body model.