3D Sign Gesture Modeling for Sign Language Training Data
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Solution Overview
Problem
Existing automated pattern and gesture recognition systems, particularly for sign language translation, face challenges in accurately and efficiently training neural networks due to the complexity of sign languages, which involve hand movements and non-manual activities, and the lack of diverse and labeled training data, leading to inefficiencies and errors in translation.
Innovation Solution
The use of three-dimensional (3D) models to simulate gestures in a virtual scene for generating training images, along with automatic labeling of features, reduces the need for manual data collection and labeling, enabling more efficient and accurate training of neural networks for sign language translation systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If manual data collection and labeling is used for training sign language translation systems, then diverse training data can be obtained, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent uses 3D models to create virtual copies of human hands and bodies that can simulate sign language gestures. These digital twins allow for automated generation of training images without requiring physical actors, thereby eliminating the time-consuming manual data collection process while maintaining data diversity through programmable variations in lighting, camera angles, and gestures.
Solution Approach 2:
The system pre-defines 3D models of hands and bodies with articulated joints and surfaces that can automatically assume various gesture positions. By preparing these models in advance with built-in gesture libraries and animation capabilities, the system can rapidly generate training data on demand without requiring real-time manual performance capture and labeling.
2Measurement precision
If more training images are generated to improve recognition accuracy, then the neural network performance improves, but the computational resources and time required increase
Solution Approach 1:
The patent implements dynamic parameter adjustment where the system automatically varies lighting conditions, camera angles, and gesture positions through programmable control of the 3D scene. This allows for efficient generation of diverse training images by systematically exploring the parameter space rather than requiring exhaustive manual capture, thereby improving accuracy without linearly increasing resource consumption.
Solution Approach 2:
The system changes physical parameters such as lighting intensity, camera position, and model orientation to generate varied training images from the same 3D assets. By manipulating these parameters algorithmically, the system can produce numerous distinct training samples without proportionally increasing computational burden, as the base 3D models remain reusable across multiple parameter configurations.
3Productivity
If 3D models are used to generate training images, then manual data collection is reduced, but the initial setup and model creation complexity increases
Solution Approach 1:
The patent creates universal 3D models of hands and bodies that can represent multiple different gestures and positions through articulation and deformation. These multi-functional models serve as reusable assets that can generate diverse training data for various sign language gestures without requiring separate physical capture sessions for each gesture, thereby reducing long-term complexity despite initial model creation effort.
Data Source
AI summary
Methods, devices and systems for training a pattern recognition system are described. In one example, a method for training a sign language translation system includes generating a three-dimensional (3D) scene that includes a 3D model simulating a gesture that represents a letter, a word, or a phrase in a sign language. The method includes obtaining a value indicative of a total number of training images to be generated, using the value indicative of the total number of training images to determine a plurality of variations of the 3D scene for generating of the training images, applying each of plurality of variations to the 3D scene to produce a plurality of modified 3D scenes, and capturing an image of each of the plurality of modified 3D scenes to form the training images for a neural network of the sign language translation system.