3D Body Model Deformation via Silhouette Vertex Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current 3D modeling techniques face challenges in accurately representing subjects using limited image data, often resulting in unrealistic or distorted models due to suboptimal mapping of silhouette pixels to vertices, especially in areas with concave and convex shapes.
Innovation Solution
The implementation of an optimization function that maps silhouette pixels to vertices in a way that distributes the mapping more evenly, avoiding clustering and unnatural artifacts by using scoring factors to penalize large ray lengths and disparities in vertex assignments, ensuring a more accurate and realistic deformation of the 3D body model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If silhouette pixels are mapped to vertices using conventional techniques, then the mapping process is simple and fast, but the resulting 3D model exhibits clustering artifacts and unrealistic distortions especially in concave and convex areas
Solution Approach 1:
The patent applies preliminary action by pre-defining scoring factors and constraints before the mapping process. The optimization function is prepared with predetermined criteria (ray length penalties, vertex assignment disparities) that guide the mapping process, allowing the system to achieve accurate results without complex real-time calculations during actual mapping.
Solution Approach 2:
The patent utilizes parameter changes by adjusting mapping parameters through the optimization function. The system modifies vertex assignments based on scoring factors that evaluate ray lengths and distribution patterns, dynamically changing mapping parameters to eliminate artifacts while maintaining overall mapping simplicity.
2Manufacturing precision
If more image data is collected to improve model accuracy, then the representation quality increases, but the data processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by focusing optimization efforts on specific problem areas rather than uniformly processing all image data. The scoring factors target localized issues such as concave and convex regions where artifacts commonly occur, allowing accurate modeling of critical areas while reducing processing requirements for less problematic regions.
Solution Approach 2:
The patent uses partial action by applying the optimization function selectively to areas where it is most needed. Rather than processing all silhouette pixels with full optimization complexity, the system concentrates computational effort on regions with high artifact probability, achieving good overall results with reduced processing time.
3Object-generated harmful factors
If the mapping distributes vertices more evenly to avoid clustering, then artifact reduction is achieved, but the mapping complexity and computational overhead increase
Solution Approach 1:
The patent implements feedback through the scoring factor mechanism. The optimization function continuously evaluates mapping quality using feedback from ray length measurements and vertex assignment patterns. This feedback loop automatically adjusts mappings to reduce clustering and artifacts without requiring complex manual intervention or overly complicated algorithms.
Solution Approach 2:
The mapping system performs self-service through the automated optimization function that evaluates and adjusts its own mappings. The scoring factors enable the system to self-correct mapping errors by identifying and redistributing problematic vertex assignments, reducing artifacts while maintaining manageable complexity through self-regulation.
Data Source
AI summary
An illustrative 3D modeling system accesses 1) a 3D body model bounded by vertices forming an unadorned body (without hair or clothing), and 2) a 2D image depicting an adorned subject (with hair and/or clothing). The set of vertices is configurable to simulate different subjects based on a set of parameters and the adorned subject is outlined in the 2D image by a set of silhouette pixels. The 3D modeling system maps the silhouette pixels to vertices of a particular cross section of the 3D body model using an optimization function configured to even out a distribution of silhouette pixels to vertices. Based on the mapping, the 3D modeling system defines the set of parameters to deform the 3D body model such that the particular cross section conforms to an outline of the adorned subject formed by the set of silhouette pixels. Corresponding methods and systems are also disclosed.