Digital Human Generation via 3D Head Point Cloud Fusion
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Solution Overview
Problem
Traditional methods for creating digital human figures are labor-intensive, costly, and result in low accuracy and poor effects, with a long production time and high investment costs.
Innovation Solution
A digital human generation method that involves acquiring a target object model from a picture, obtaining a point cloud of head key features from a pre-configured feature library, and fusing these point clouds to generate a digital human figure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional manual methods are used to create digital human figures, then high quality and realism can be achieved, but the production time and cost increase significantly
Solution Approach 1:
The patent uses 3D scanning technology to create digital copies of real human figures, capturing geometric information directly from physical objects. This copying approach replaces manual modeling while maintaining high fidelity, resolving the contradiction between quality and production time by automatically replicating real-world features.
Solution Approach 2:
The patent replaces manual mechanical modeling processes with automated 3D scanning and point cloud processing systems. The mechanical action of hand-modeling is substituted by optical scanning and computational algorithms, dramatically reducing production time while preserving detail quality.
2Manufacturing precision
If traditional manual methods are used to create digital human figures, then high quality and realism can be achieved, but the investment cost increases significantly
Solution Approach 1:
By using 3D scanning to copy real human figures directly, the patent eliminates the need for expensive manual labor across multiple stages (modeling, binding, animation). The automated copying process maintains quality while reducing the investment required for professional human resources.
Solution Approach 2:
The system enables self-service digital human creation where the 3D scanning and point cloud processing automatically generate high-quality results without requiring professional operators. This self-service capability reduces dependency on expensive expert labor while maintaining manufacturing precision.
3Productivity
If automated methods are used to create digital human figures, then production time and cost are reduced, but the accuracy and realism decrease
Solution Approach 1:
The patent uses high-precision 3D scanning to create accurate digital copies of real human figures. The copying process captures fine geometric details automatically, maintaining manufacturing precision while achieving high productivity through automation.
Solution Approach 2:
The patent transitions from 2D images to 3D point cloud data, adding a dimensional aspect that preserves spatial accuracy and realism. This dimensional enhancement allows automated processing to maintain geometric fidelity while improving generation efficiency.
4Manufacturing precision
If traditional manual methods are used, then detailed control over character features is possible, but the complexity and number of operation stages increase
Solution Approach 1:
The patent copies character features directly from 3D scanned data, preserving detailed geometric information automatically. This eliminates the need for complex manual modeling processes while maintaining feature accuracy, as the copying process inherently captures all spatial details.
Solution Approach 2:
The patent extracts key geometric features directly from 3D scanned point cloud data, isolating the essential character information needed for digital human creation. This extraction approach simplifies the overall process by removing unnecessary intermediate steps while preserving feature accuracy.
Data Source
AI summary
A digital human generation method, an electronic device and a storage medium are disclosed. The solution relates to the fields of augmented reality technologies, virtual reality technologies, computer vision technologies, deep learning technologies, or the like, and can be applied to scenarios, such as metaverse, a virtual digital human, or the like. An implementation includes: acquiring a corresponding target object model based on a picture of a to-be-generated digital human; acquiring a corresponding point cloud of a head key feature in the picture from a pre-configured feature library based on the head key feature; and fusing the point cloud of the head key feature in the target object model to obtain a digital human figure.