Hybrid Gesture Generation Model for AI Avatars
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Solution Overview
Problem
Existing gesture generation methods fail to handle words not in the training set, treating them as unknown tokens, and are limited to generating co-speech gestures suitable for general conversations, lacking specificity for unique situations like greetings or shaking hands.
Innovation Solution
A hybrid gesture generation model using a sentence encoder that outputs embeddings for sentences containing any word, even if it's not in the training set, combined with a retrieval system for pre-defined special gestures for specific situations, allowing for both co-speech and special gesture generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If individual words are used as input to generate gestures, then the model can process text at word level, but words not in the training set are treated as unknown tokens and different words receive the same encoding
Solution Approach 1:
The patent segments the input text into sentences rather than individual words, and uses sentence-level embeddings to capture the overall meaning. This allows the model to generate gestures based on the complete sentence context rather than isolated words, improving both accuracy and handling of unseen vocabulary.
Solution Approach 2:
The patent introduces sentence embeddings as an intermediary representation between the input text and the gesture generation model. These embeddings capture the semantic meaning of sentences including unseen words, acting as a bridge that preserves meaning while being robust to vocabulary variations.
2Adaptability or versatility
If the model is trained only on co-speech gesture data, then it can generate gestures for general conversations, but it cannot generate appropriate gestures for specific situations like greetings or shaking hands
Solution Approach 1:
The patent implements a dynamic gesture generation system that adapts its behavior based on the input sentence. The model dynamically selects between generating co-speech gestures or retrieving situation-specific gestures by analyzing the sentence embedding and comparing it against stored situation prototypes, allowing flexible adaptation to different contexts.
Solution Approach 2:
The patent changes the operational parameters of the gesture generation system based on the detected situation. When a specific situation is detected (e.g., greeting, handshake), the system switches from generation mode to retrieval mode, fundamentally changing how gestures are produced to ensure appropriateness for the specific context.
3Measurement precision
If a hybrid model with sentence encoder and retrieval system is used, then both co-speech and special gestures can be generated accurately, but the system complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing situation prototypes and their associated gestures during the training phase. This allows the runtime system to simply compare incoming sentence embeddings against these pre-computed prototypes and retrieve appropriate gestures, significantly reducing runtime complexity while maintaining high accuracy.
Solution Approach 2:
The patent creates a universal sentence encoder that serves multiple functions: it encodes sentences for both co-speech gesture generation and situation-specific gesture retrieval. This multi-functional component reduces overall system complexity by eliminating the need for separate processing pipelines for different gesture types.
Data Source
AI summary
Embodiments described herein include a hybrid gesture generation model using a sentence encoder. By using a pre-trained model as the sentence encoder, the framework may output an embedding for a sentence containing any word, even if it is not in the training set, Further, embodiments described herein include a hybrid gesture model that combines trained co-speech gesture generation with a retrieval of pre-defined special gestures. The generation part uses text input to a model trained with co-speech gesture data. The retrieval part uses pre-defined gestures for six different situations that have been prepared in advance. Using embodiments described herein, an AI avatar can perform special gestures like greeting or shaking hands in predefined specific situations, and co-speech gestures in other conversational situations.