WordGesture-GAN: Modeling Word-Gesture Movement with Generative Adversarial Network

Document Title

WordGesture-GAN: Modeling Word-Gesture Movement with Generative Adversarial Network

Document Information

• Field of Study: Human-Computer Interaction (HCI), Machine Learning, Gesture Input Technology
• Keywords: GAN, Word-Gesture Input, Touch Gesture, Mobile Devices, Variational Auto-Encoder, Keyboard Layout Optimization, Gesture Typing, Temporal Modeling

Research Background and Problem Statement

• Identified Problems or Challenges:

  1. Existing gesture generation models (e.g., Minimum Jerk Model and Style-Transfer GAN) have limitations in generating gestures based on spatial and temporal information, failing to fully simulate the natural variations of human input.
  2. Gesture input generation models need to produce realistic and variable gestures for arbitrary words to train and test gesture decoders effectively.

• Significance: These models play a critical role in training language input interfaces, optimizing virtual keyboard layouts, and developing efficient gesture input technologies, directly impacting user input speed and accuracy.

• Research Motivation and Related Work:

  1. Word-gesture input has been widely applied in consumer products (e.g., Google’s Gboard and Microsoft’s SwiftKey), but its development still requires extensive data support.
  2. Previous models only address spatial information (e.g., Style-Transfer GAN) or idealized trajectories (e.g., Minimum Jerk Model), failing to capture the dynamic and natural variability in user input gestures.

Solution

• Proposed Method or Solution:

  • WordGesture-GAN, a Conditional Generative Adversarial Network (Conditional GAN), achieves:
    1. Extraction of embedded variability from user-drawn gestures using a Variational Auto-Encoder (VAE);
    2. Generation of realistic gestures incorporating temporal (timestamps) and spatial (touch coordinates) dimensions.

• Innovations:

  • Integration of VAE into the GAN framework to generate gestures with natural variability without relying on user reference input.
  • Simulation of gestures in both spatial and temporal dimensions (e.g., (x, y, t)), surpassing existing models that only describe spatial trajectories.
  • Introduction of variability through Gaussian distribution sampling without dependence on user reference gestures.

• Implementation Steps and Key Techniques:

  1. Construct gesture prototypes (character connection lines based on virtual keyboard) and encode user gesture data using VAE;
  2. Generate gestures using GAN, combining prototypes with Gaussian distribution sampling to produce variable data;
  3. Optimize model performance through Wasserstein distance, reconstruction loss, and Kullback-Leibler (KL) divergence.

Research Outcomes

• Specific Results:

  • WordGesture-GAN generates gestures that are closer to real user gestures compared to existing models (Minimum Jerk Model and Style-Transfer GAN).
  • Demonstrates superior performance in evaluation metrics such as L2 and Dynamic Time Warping Wasserstein distance, and Frechet Inception Distance (FID).

• Advantages Over Existing Solutions:

  • Simultaneously generates spatial and temporal dimension data, better approximating actual user input behavior.
  • Generates gestures flexibly and with higher diversity without relying on user reference gestures.

• Experimental or Evaluation Results:

  • In a dataset of 38k gestures, WordGesture-GAN-generated gestures outperform other models in visual matching, dynamic time segmentation, and speed/acceleration distribution.
  • Incorporating GAN-generated gestures into gesture input decoders (e.g., SHARK2) effectively reduces decoding error rates.

• Limitations and Future Directions:

  1. Limitations: The diversity of generated gestures is relatively low, failing to fully cover the variability distribution of user-drawn gestures; gesture time prediction is slightly slower compared to traditional models (e.g., CLC model).
  2. Future Directions:
     • Incorporate outputs from the Minimum Jerk Model as GAN input to improve gesture diversity.
     • Apply the model to more complex scenarios (e.g., multi-finger gestures, eye-tracking input) to expand its capabilities.
     • Explore real-time performance and applicability in keyboard layout optimization.

This study demonstrates the potential and practical value of deep learning technologies in the HCI domain, particularly in gesture input optimization and interface development.