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SSGesture: Multimodal Gesture Generation Framework for Human Animation Synthesis.
IEEE Computer Graphics and Applications
|June 6, 2025
Summary
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.
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.
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