Emotionally Expressive AI Performance Generation via Mood Drivers
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Solution Overview
Problem
Conventional AI-generated performances lack emotional expression and adaptability, failing to credibly depict mood and emotional states, making them less nuanced and dynamic compared to human performances.
Innovation Solution
A Bayesian-knowledge driven approach combined with a data-driven system, using mood drivers to inform the generation of performative sequences, allowing for real-time adaptation and human interpretability, enabling the creation of emotionally expressive actions in various domains like music, animation, and robotics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single synthesized persona is used in AI-generated performances, then the system complexity is reduced and generation is simpler, but the performance lacks distinctive personality and emotional expression
Solution Approach 1:
The persona is segmented into multiple distinct personas, each representing different emotional states or personality traits. Instead of using a single synthesized persona, the system maintains a collection of personas that can be selectively applied based on the desired emotional expression, thereby resolving the contradiction between simplicity and emotional capability.
Solution Approach 2:
The system dynamically selects and transitions between different personas based on contextual requirements. This dynamic persona selection allows the AI-generated performance to adapt its emotional expression in real-time, overcoming the limitation of a static single persona while maintaining manageable system complexity through on-demand persona activation.
2Productivity
If conventional AI methods are used for performance generation, then the generation process is more straightforward and faster, but the performance lacks nuance, variety, and dynamic emotional states
Solution Approach 1:
Multiple personas are pre-defined and prepared in advance, each encapsulating specific emotional states and behavioral characteristics. This preliminary preparation allows the system to quickly select from pre-configured options during performance generation, maintaining high productivity while achieving nuanced and varied emotional expression through the ready-made persona library.
3Reliability
If AI-generated performances use fixed patterns, then the generation process is more efficient and predictable, but the performance lacks dynamic adaptation and emotional inflection
Solution Approach 1:
The system incorporates feedback mechanisms that monitor the current performance state and contextual information, then use this feedback to dynamically select appropriate personas. This feedback-driven persona selection maintains reliability by following structured decision-making processes while enabling real-time emotional adaptation through responsive persona switching based on performance needs.
Data Source
AI summary
A system includes a computing platform having a hardware processor and a memory storing software code and a machine learning (ML) model trained to predict the next element of a sequence. The software code is executed to receive input data identifying an element of the sequence, determine, using the input data, at least one mood driver(s) of the sequence, and predict, based on input data and the mood driver(s), one or more candidate next element(s) of the sequence using the ML model. The software code further obtains expertise data relating to the sequence, evaluates the candidate next element(s), using the expertise data, the input data, and the mood driver(s), to provide aptness score(s) each corresponding to a respective one candidate next element, and determines, using the aptness score(s) and a respective probability assigned to each of the candidate next element(s) by the ML model, the next element of the sequence.