ML-Based 3D Digital Item Fitting for Character Models
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Solution Overview
Problem
Current systems for fitting digital items to different character models in three-dimensional animation are inefficient, as they often require manual adjustment and struggle to account for artistic traits specific to individual character models, leading to issues with symmetry and clipping.
Innovation Solution
A machine learning system that uses a training dataset of model-specific vertices for different character models to compute the shape and size of digital items for a new character model, applying post-processing techniques to enforce symmetry and correct clipping issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated fitting systems are used to fit digital items to character models, then the time needed to implement new digital items is decreased, but the systems are unable to account for artistic traits specific to individual character models and create issues with symmetry and clipping
Solution Approach 1:
The system segments the fitting process into multiple stages: an automated fitting stage that rapidly positions digital items on character models, followed by a post-processing stage that corrects symmetry and clipping issues. This segmentation allows the system to benefit from both automated efficiency and manual-quality precision for different aspects of the fitting process.
Solution Approach 2:
The system performs preliminary automated fitting to establish a baseline configuration before applying post-processing corrections. By doing the rough positioning work first and then refining the results, the system avoids the time cost of entirely manual fitting while still achieving high-quality final results.
2Manufacturing precision
If manual adjustment is used to preserve artistic traits and symmetry of digital items, then the quality of fitting is improved, but the time and effort required increases significantly
Solution Approach 1:
The post-processing system automatically detects and corrects symmetry and clipping issues without requiring manual intervention. The system serves itself by identifying problems through analysis of the fitted digital items and autonomously applying corrections, eliminating the need for time-consuming manual adjustments while maintaining high quality results.
3Manufacturing precision
If digital items are modeled multiple times to fit different character models in interactive media, then the fit quality for each character is improved, but the technical cost and time to update the project increases exponentially
Solution Approach 1:
The system creates a universal automated fitting pipeline that can handle multiple character models and various types of digital items (armor, clothing, accessories) through a single unified process. This multi-functional system eliminates the need to create separate modeling workflows for different character types, reducing project complexity while maintaining high fit quality across all characters.
Solution Approach 2:
The system adapts digital items to different character models by adjusting parameters such as scale, rotation, and position through automated algorithms, rather than requiring complete remodeling. The post-processing stage further adjusts symmetry and clipping parameters automatically, allowing the same digital item to fit multiple character types with minimal parameter changes instead of requiring multiple complete models.
Data Source
AI summary
Systems and methods for modifying three-dimensional digital items to fit different character models are described herein. A machine learning system may be configured to compute a shape and a size of three-dimensional digital objects fit for a second character model based on a shape and a size of the three-dimensional digital objects fit for a first character model. Using the machine learning system, a base transform matrix may be generated, which corresponds to a first exemplary three-dimensional digital object fit for the first character model and a second exemplary three-dimensional digital object fit for the second character model. The machine learning system may be trained using the base transform matrix and machine-learning training data. Input data may be received from a client computing device, where the input data defines a plurality of input vertices for an input three-dimensional digital object fit for the first character model. Using the machine learning system, output data may be generated that defines a plurality of output vertices for an output three-dimensional digital object for the second character model.