3D Model Generation from 2D Sketches via Vertex Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current 3D modeling processes are time-consuming and expensive, requiring skilled experts and accurate 2D sketches with light sources and shadows to produce high-quality 3D models, making them cumbersome and often necessitating human intervention.
Innovation Solution
A system utilizing a parameterized humanoid engine, 3D vertex plotting engine, and texturizing engine, including deep learning models, to generate 3D models from mathematically inaccurate 2D sketches by predicting 3D vertices, rendering rough models, and adding textures, allowing for realignment and accurate fitting without the need for expensive software tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual 3D sculpting is performed by skilled experts, then high-quality 3D models are produced, but the process is time-consuming and requires significant human intervention
Solution Approach 1:
The patent replaces manual mechanical 3D sculpting with an automated system that uses machine learning models (parameterized humanoid engine, 3D vertex plotting engine, texturizing engine) to generate 3D models from 2D sketches, eliminating the need for skilled experts to perform time-consuming manual sculpting while maintaining high model quality
Solution Approach 2:
The system enables automatic generation of 3D models from 2D sketches through self-contained machine learning engines that autonomously perform vertex prediction, model plotting, and texturizing without requiring human intervention or expert input, making the process self-service and highly efficient
2Productivity
If automated 3D modeling software tools are used, then the process is less time-consuming, but the tools are expensive and require mathematically accurate 2D sketches with light sources and shadows
Solution Approach 1:
The patent employs cost-effective machine learning models that can be deployed as software services, replacing expensive commercial 3D modeling tools. The system processes simple 2D sketches without requiring complex inputs like light sources and shadows, making the approach both economically viable and technically accessible
Solution Approach 2:
The system changes the input parameter requirements by accepting mathematically inaccurate 2D sketches without light source or shadow information, unlike traditional software that requires precise technical drawings. The machine learning models are trained to infer 3D geometry from simplified 2D inputs, fundamentally changing what constitutes adequate input data
3Ease of operation
If mathematically inaccurate 2D sketches without light sources or shadows are provided to traditional software tools, then the input process is simplified, but the tools fail to generate accurate 3D models
Solution Approach 1:
The patent replaces traditional geometric processing algorithms with machine learning-based engines that can interpret and reconstruct 3D models from mathematically inaccurate 2D sketches. The parameterized humanoid engine and texturizing engine use learned patterns to infer accurate 3D geometry and textures even when input sketches lack precise mathematical properties, light sources, or shadow information
Data Source
AI summary
An application server system and a method for generating a three-Dimensional (3D) model from 2D humanoid sketches is provided. The method includes receiving, by a parameterized humanoid engine, the 2D humanoid sketches from a user device associated with a user, predicting 3D vertices that correspond to the 2D humanoid sketches based on a pose, shape, and camera orientation of a subject in the 2D humanoid sketches, plotting, using a 3D vertex plotting engine, the 3D vertices to obtain a rough 3D model, rendering the rough 3D model onto a user interface of an application server system or the user device, enabling the user to realign the rough 3D model, thereby obtaining a realigned 3D model that is accurately fit the rough 3D model to the 2D humanoid sketches, and adding textures to the realigned 3D model to generate the 3D model of the subject in the 2D humanoid sketches.