3D Digital Item Adaptation via Machine Learning Vertex Regression
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Solution Overview
Problem
Current digital animation processes require extensive modeling of items for various character models, leading to increased technical costs and creative constraints, as each new item or character model necessitates multiple versions to fit different characters, discouraging the creation of radically different models due to the inefficiency and subjectivity in digital modeling.
Innovation Solution
A machine learning system is developed to compute the shape and size of three-dimensional digital items to fit different character models by training on existing data, using vertex matching and regression models to generate output vertices for new items based on input vertices from a different character model, allowing for efficient adaptation of digital items across multiple character models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple versions of digital items are modeled to fit different character models, then the adaptability of digital items is improved, but the device complexity and time required for modeling increase significantly
Solution Approach 1:
The patent applies universality by creating a single parametric digital item model that can be adapted to multiple character models through parameter adjustment rather than creating separate models for each character. The system uses a unified modeling approach where one digital item design can serve multiple character types by modifying parameters such as size, shape, and fit characteristics.
Solution Approach 2:
The patent implements parameter changes by systematically varying geometric and dimensional parameters of digital items to achieve proper fit across different character models. The system adjusts parameters like scale, position, rotation, and morphing values to adapt a single digital item to various character geometries without requiring complete remodeling.
2Adaptability or versatility
If multiple versions of digital items are created for different character models, then the adaptability is improved, but the time required for modeling increases
Solution Approach 1:
The patent applies preliminary action by pre-defining parameter relationships, correspondence mappings, and adaptation rules that enable rapid digital item fitting to different character models. The system establishes beforehand the geometric correspondences between different character models and the parameters that control item fitting, allowing quick adaptation without time-consuming manual modeling for each character.
Solution Approach 2:
The patent uses copying by creating reusable digital item templates and parameter sets that can be copied and adapted across different character models. Instead of modeling each item from scratch for every character, the system copies base digital item designs and applies parameter transformations to achieve proper fit, dramatically reducing modeling time.
3Ease of manufacture
If digital modeling is performed manually by artists, then the subjective styling can be maintained, but the productivity and consistency decrease
Solution Approach 1:
The patent introduces an intermediary system that acts as a bridge between manual artistic input and automated adaptation. The system uses intermediate representations such as parameterized models, correspondence maps, and transformation rules that capture artistic intent while enabling automated processing. This intermediary layer allows artists to define styling once at a high level while the system handles the detailed adaptation work.
4Adaptability or versatility
If new character models with radically different features are created, then the creativity is improved, but the difficulty of fitting existing digital items increases
Solution Approach 1:
The patent applies segmentation by breaking down the digital item and character model geometries into corresponding vertex sets and geometric features that can be independently analyzed and mapped. The system segments the adaptation problem into identifying correspondence relationships between different geometric elements, allowing systematic handling of radically different character features through localized parameter adjustments rather than global remodeling.
Data Source
AI summary
Systems and methods for modifying three-dimensional digital items to fit different character models are described herein. In an embodiment a machine learning system is configured to compute a shape and size of three-dimensional digital objects to fit a second character model based on the shape and size that the same three-dimensional digital objects have to fit a first character model. A server computer receives particular input data defining a plurality of particular input vertices for a particular input three-dimensional digital object fit for the first character model. In response to receiving the particular input data, the server computer computes, using the machine learning system, particular output data defining a plurality of particular output vertices for a particular output three-dimensional digital object, the particular output three-dimensional digital object comprising the particular input three-dimensional digital object fit for the second character model. The server computer then causes displaying, on the client computing device, of the particular output three-dimensional digital object combined with the second character model.