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Related Experiment Video

Updated: May 29, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

SSGesture: Multimodal Gesture Generation Framework for Human Animation Synthesis.

Xinyi Wang, Shiguang Liu, Xu Yang

    IEEE Computer Graphics and Applications
    |June 6, 2025
    PubMed
    Summary
    This summary is machine-generated.

    We developed SSGesture, a diffusion-based framework for generating realistic dialogue gestures for virtual characters. This approach enhances animation production efficiency by improving contextual variation and reducing jitter in generated gestures.

    Related Experiment Videos

    Last Updated: May 29, 2026

    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
    08:15

    Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

    Published on: March 28, 2025

    Area of Science:

    • Computer Graphics
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Technological advancements are transforming animation production.
    • Generating realistic dialogue gestures for virtual characters is a significant challenge.
    • Existing methods lack multimodal integration, leading to unnatural gestures with low variation and jitter.

    Purpose of the Study:

    • To propose SSGesture, a novel diffusion-based framework for automatic dialogue gesture generation.
    • To improve contextual variation and reduce jitter in generated gestures.
    • To enhance the efficiency of human animation production.

    Main Methods:

    • Utilized a diffusion-based framework to capture cross-modal associations.
    • Implemented a three-layer attention structure for enhanced multimodal processing.
    • Introduced interpolation-based gesture style control to resolve style conflicts.
    • Developed a unified unmarked style prompt structure using prompt-aligned adaptive normalization.

    Main Results:

    • SSGesture effectively captures cross-modal associations for gesture generation.
    • The framework demonstrates improved contextual variation and reduced jitter in gestures.
    • User studies confirm substantial enhancement in human animation production efficiency.

    Conclusions:

    • SSGesture offers a novel solution for automatic dialogue gesture generation.
    • The proposed framework addresses limitations of current approaches by integrating multimodal information.
    • SSGesture has practical applications in intelligent virtual assistants and human animation synthesis.