3D Asset Generation from 2D Images Using ML Mesh Deformation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for generating 3D assets from 2D images in virtual environments are inefficient, requiring extensive computational resources and time, especially when simulating complex objects, and often result in poor user experience due to latency and high costs associated with traditional art and animation teams.
Innovation Solution
A system and method using trained machine learning models to generate 3D assets by deforming and posing a rigged 3D mesh based on a template mesh, with texture mapping from 2D images, allowing for efficient creation of realistic and immersive 3D assets with limited training data, suitable for online virtual experiences and assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to generate 3D assets from 2D images, then manufacturing precision and reliability are maintained, but productivity is low and loss of time is high
Solution Approach 1:
The system performs preliminary actions by pre-processing the 2D image to extract key features, edges, and semantic information before 3D reconstruction. This preliminary analysis enables the subsequent generation process to proceed more efficiently with fewer iterative adjustments needed, thereby reducing overall generation time and computational resource requirements
Solution Approach 2:
The patent introduces an intermediary representation layer between 2D images and 3D assets. This intermediary involves converting 2D images into intermediate representations such as depth maps, normal maps, or feature embeddings that bridge the gap between 2D input and 3D output, enabling faster and more accurate reconstruction without requiring extensive training data
2Ease of manufacture
If traditional art and animation teams are used, then manufacturing precision is high, but device complexity and loss of substance increase
Solution Approach 1:
The system enables self-service by allowing automatic generation of 3D assets directly from 2D images without requiring human artists or animation teams. The automated pipeline performs segmentation, mesh generation, texturing, and animation creation autonomously, eliminating the need for complex human resource coordination and significantly simplifying the production process
Solution Approach 2:
The patent replaces the mechanical system of human artists manually creating 3D assets with an automated computational system. Machine learning models and algorithms substitute for human creativity and technical skill in modeling, texturing, and animating, transforming a labor-intensive manual process into an automated digital workflow that reduces organizational complexity
3Manufacturing precision
If extensive training data is used, then manufacturing precision improves, but loss of substance and productivity worsen
Solution Approach 1:
The system extracts only the essential features and information needed for 3D asset generation from the input 2D images, rather than processing entire datasets. By selectively extracting key geometric, textural, and semantic features, the system achieves high manufacturing precision while minimizing computational resource consumption and avoiding the need for extensive training data
Solution Approach 2:
The patent applies local quality by focusing computational resources on critical regions of the 3D asset that require high precision, such as facial features, object boundaries, or areas with complex geometry. Less critical regions use simplified processing, thereby achieving overall high realism without uniformly consuming excessive computational resources across the entire asset
Data Source
AI summary
Some implementations relate to methods, systems, and computer-readable media to generate 3D assets from 2D images. In some implementations, a computer-implemented method to generate a 3D asset for an object using a trained machine learning model includes providing a 2D image of the object as input to the trained machine learning model, obtaining a template 3D mesh and a representative of a class of objects of interest that includes the object, generating based on the template 3D mesh and the representative of the class, a rigged 3D mesh for the object, deforming and posing the rigged 3D mesh to match the 2D image, and applying a texture extracted from the 2D image to the deformed and posed 3D mesh to create the 3D asset of the object.