Music-Reactive Character Animation Using Neural Dance Generation
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Solution Overview
Problem
Existing technologies lack the ability to generate realistic and creative dance choreography synchronized with music in real-time using machine learning, limiting the interactive and expressive capabilities of computer-animated avatars.
Innovation Solution
Utilizing an encoding and decoding neural network based on a conditional recursive neural network model to generate animated characters that dance in real-time to music, with the model trained to produce synchronized and creative dance moves by standardizing music features and pose data, allowing for randomization and seamless transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional animation methods are used, then control and predictability are maintained, but realism and creativity in dance choreography are limited
Solution Approach 1:
The patent transforms the animation control approach by changing parameters from fixed keyframe values to probabilistic distributions. The neural network outputs mean and variance vectors that define dance pose distributions, allowing the system to generate varied, creative choreography while maintaining control through the structured probability space. This enables realistic, natural-looking dance movements that adapt to music input.
2Ease of operation
If real-time generation is implemented, then interactive capability is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-training the neural network model offline to learn the mapping between music features and dance choreography. The model is trained on datasets of music-audio pairs with corresponding dance sequences, enabling it to capture complex patterns beforehand. During real-time operation, only inference is required, which is computationally efficient and enables interactive capability without excessive complexity.
Solution Approach 2:
The patent replaces traditional mechanical animation systems (keyframe interpolation, motion capture) with a neural network-based generative model. This substitution allows the system to generate novel dance choreography directly from music input without requiring pre-defined animation rigs or capture equipment, enabling real-time interactive animation with creative flexibility.
3Manufacturing precision
If synchronized dance to music is achieved, then artistic quality is improved, but processing time increases
Solution Approach 1:
The patent replaces traditional audio-reactive animation systems that rely on frame-by-frame audio analysis and synchronous rendering with a neural network model that processes music features and generates dance poses in a single integrated computation. The model takes music input features and directly outputs synchronized dance choreography, achieving high synchronization quality without sequential processing delays.
Data Source
AI summary
Example methods for generating an animated character in dance poses to music may include generating, by at least one processor, a music input signal based on an acoustic signal associated with the music, and receiving, by the at least one processor, a model output signal from an encoding neural network. A current generated pose data is generated using a decoding neural network, the current generated pose data being based on previous generated pose data of a previous generated pose, the music input signal, and the model output signal. An animated character is generated based on a current generated pose data; and the animated character caused to be displayed by a display device.