3D Body Modeling via 2D Image Reconstruction
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Solution Overview
Problem
Current three-dimensional modeling of the human body requires expensive sensors and high-friction body measurements, making it inconvenient for widespread use in fitness applications, and existing systems lack efficient methods for guiding users in body transformation journeys.
Innovation Solution
A system utilizing two-dimensional body images captured by portable devices, processed to generate personalized three-dimensional body models, allowing users to interact with and adjust their models to set targets, and providing a guided body change journey through nutrition, exercise, and sleep recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive sensors such as stereo imaging elements, three-dimensional scanners, or depth sensing devices are used for three-dimensional body modeling, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses two-dimensional images as copies or projections of the three-dimensional body, which can then be processed to reconstruct the 3D model. Instead of directly capturing 3D data with complex sensors, the system captures simple 2D images and computationally derives 3D information, significantly reducing hardware complexity while maintaining modeling accuracy
Solution Approach 2:
The patent replaces complex mechanical sensing systems (stereo cameras, depth sensors, scanners) with a computational approach using standard 2D imaging and image processing algorithms. The mechanical complexity of 3D sensing is substituted with optical capture followed by digital reconstruction, eliminating the need for specialized expensive hardware
2Measurement precision
If manual body measurements and data entry are required for fitness tracking, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs automatic body measurement extraction from 2D images without requiring manual intervention. The computer vision algorithms automatically detect body contours, calculate measurements, and generate 3D models, allowing the system to serve itself rather than requiring user manual measurement and data entry
Solution Approach 2:
The patent replaces manual mechanical measurement processes (tapes, rulers, physical measurement) with automated optical capture and digital image analysis. The system substitutes human-operated measurement tools with computer vision algorithms that automatically extract precise body measurements from photographs
3Reliability
If high-friction body measurements and coordination with health experts are required, then reliability is improved, but ease of operation deteriorates
Solution Approach 1:
The system provides automated fitness tracking and monitoring capabilities that do not require coordination with health experts. The computer vision system independently captures, processes, and analyzes body data, generating fitness insights and progress tracking without human intervention, thereby maintaining reliability while dramatically improving ease of operation
Solution Approach 2:
The patent introduces an automated computer vision system as an intermediary between the user and fitness tracking goals. This intermediary automatically performs measurement extraction, 3D modeling, and fitness analysis, eliminating the need for direct user coordination with health experts while maintaining or improving tracking reliability through consistent automated processing
Data Source
AI summary
Described are systems and methods directed to a body change journey that utilizes collected two-dimensional (“2D”) body images of a body of a user, generation and presentation of a personalized three-dimensional (“3D”) body model of the body of the user based on those 2D body images, the generation and presentation of different predicted personalized 3D body models of the body of the user at different body measurements (e.g., different body fat percentages, different muscle mass amounts, different body weights, etc.), and the generation of a body change journey that guides the user from their current body model to a selected target body model that is represented by a target personalized 3D body model of the body of the user.