Virtual Digital Human Generation Using Body Proportions for Fitness Alignment
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Solution Overview
Problem
In human-computer interaction technology, fitness videos played on electronic devices result in inconsistent fitness movements among users, leading to a poor fitness experience due to varying interpretations of the same actions.
Innovation Solution
A method and apparatus for generating a virtual digital human by performing human key recognition on frame images to determine actual and predicted body part lengths, using a target proportional relationship, and drawing a virtual digital human based on actual lengths, drawing height, and position information to ensure consistency with user movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users follow the same fitness video, then the video provides standardized fitness guidance, but users perform movements with significant differences resulting in poor fitness effect
Solution Approach 1:
The system captures users' actual movements through image acquisition devices and provides real-time feedback by generating virtual digital humans that mirror the users' poses. This feedback loop enables users to see their movement alignment with the fitness video and adjust accordingly, resolving the contradiction between standardized guidance and individual movement variations.
Solution Approach 2:
The system creates a virtual copy (digital human) of the user's body based on captured images and key point recognition. This virtual replica is then used to generate standardized fitness action diagrams that can be overlaid or compared with the user's actual movements, enabling precise measurement of movement alignment while maintaining the benefits of standardized fitness guidance.
2Measurement precision
If virtual digital human is generated based on actual body measurements, then the visual reference accuracy is improved, but the system complexity increases
Solution Approach 1:
The system extracts only the essential information needed for generating the virtual digital human - specifically key point positions and body part lengths from captured images. By focusing on these critical parameters rather than processing complete image data, the system achieves high visual reference accuracy while managing computational complexity.
Solution Approach 2:
The system segments the human body into distinct key points and body parts (head, torso, limbs, etc.) for independent measurement and representation. This segmentation allows the complex task of generating an accurate virtual digital human to be broken down into manageable steps, each handling specific body regions, thereby reducing overall system complexity.
Data Source
AI summary
Provided are a virtual digital human generation method and apparatus, and an electronic device. The method includes: acquiring a first image frame collected by an image collection apparatus when a target video is played; performing human body key identification on the first image frame, to determine position information between human body key points, a first actual length of a target body part, and a second actual length of a body part other than the target body part; on the basis of a target proportional relationship and the first actual length, determining a predicted length of the body part other than the target body part; on the basis of the second actual length and the predicted length, determining a drawing height of the other body part; and performing drawing on the basis of the first actual length, the drawing height and position information, to generate a virtual digital human.