Text-Driven Gesture Generation With Variable-Length 3D Motion
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Solution Overview
Problem
Current methods for gesture generation are constrained to a fixed length of generated frames, resulting in unnatural gestures and are incapable of producing common, specific gestures like wave, handshake, and nod, limiting the interaction effectiveness of human-like agents such as virtual avatars and social robots.
Innovation Solution
A deep learning-based model using a gate recurrent unit-based autoregressive model for gesture generation, combined with text classification and data augmentation, to produce variable length gestures by incorporating a stop token mechanism for frame termination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If current methods for gesture generation are used, then gesture generation is simple, but the generated gestures are unnatural and fixed in length
Solution Approach 1:
The gesture generation process is segmented into multiple independent components: text encoding module, gesture decoding module, and motion prediction module. Each module handles specific aspects of the generation process, allowing for specialized optimization while maintaining overall system manageability despite increased complexity.
Solution Approach 2:
The system transitions from fixed-length gesture generation to variable-length generation by introducing dynamic stopping conditions based on text length and gesture completion criteria. The generation process adapts its duration dynamically rather than being constrained to predetermined frame counts.
2Adaptability or versatility
If current gesture generation methods are used, then the system is easy to operate, but it cannot produce specific gestures like wave, handshake, and nod
Solution Approach 1:
The system employs specialized gesture decoding modules that focus on generating specific types of gestures (wave, handshake, nod, etc.) with dedicated motion prediction networks for each gesture category. This localized specialization enables diverse gesture types while maintaining operational simplicity through automated selection.
Solution Approach 2:
The unified architecture serves multiple functions: text-to-gesture conversion, multiple gesture type generation, and variable-length output. A single system handles diverse gesture requirements through modular components that can be selectively activated based on the desired gesture type.
3Manufacturing precision
If fixed-length gesture frames are generated, then the generation process is straightforward, but the gestures appear unnatural
Solution Approach 1:
The system generates variable-length gesture sequences that adapt to the content length and complexity. The stopping condition mechanism dynamically determines when gesture generation should terminate, allowing gestures to naturally conclude rather than being forcibly truncated or extended to fit fixed frame constraints.
Solution Approach 2:
The system changes key parameters including sequence length, motion intensity, and temporal dynamics based on the input text characteristics. These parameter adjustments enable realistic gesture variation while maintaining a unified generation framework that manages complexity.
4Reliability
If gesture generation is integrated with speech synthesis, then human-like agent interaction is enhanced, but the system complexity increases
Solution Approach 1:
The gesture generation system is merged with the speech synthesis pipeline, sharing common components such as text encoding and sequence generation mechanisms. This integration creates a unified human-like agent interaction system while avoiding complete redundancy through component sharing.
Data Source
AI summary
Embodiments described herein provide systems and methods for gesture generation from text. A method for gesture generation includes receiving an input text. The method may further include generating, via an encoder, an action representation in an action representation space based on the input text. The method may further include generating, via a first motion decoder, a first body configuration based on the action representation. The method may further include generating, via a second motion decoder, a second body configuration based on the first body configuration. The method may further include generating, via a token decoder, a first stop token based on the first body configuration.