Mixture-of-Experts Network for Seamless Character Animation Transitions
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Solution Overview
Problem
Video games often lack animated actions for characters, with available animations being disjointed and unable to be performed in succession, leading to monotony and underwhelming spectating experiences.
Innovation Solution
A machine learning framework that uses reinforcement learning and a layer-wise mixture-of-experts network to control animated models in video games, enabling them to perform multiple distinct actions and seamless transitions between these actions, improving animation quality and user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pre-recorded motion capture sequences are used for character animations, then animation production is simplified, but the animations become disjointed and unable to be performed in succession
Solution Approach 1:
The patent replaces the mechanical system of pre-recorded motion capture sequences with a machine learning-based animation generation system. The neural network learns from motion capture data to generate continuous, physically-plausible animations dynamically, eliminating the discontinuities inherent in pre-recorded sequences while maintaining production efficiency.
Solution Approach 2:
The patent introduces dynamics by transitioning from static pre-recorded animations to dynamic, real-time animation generation. The system adapts animations based on current game states and character conditions, enabling seamless transitions and continuous action sequences that respond naturally to gameplay scenarios.
2Adaptability or versatility
If more animated actions are added to video games, then character versatility improves, but animation complexity and management difficulty increase
Solution Approach 1:
The patent implements a universal animation generation system that can produce multiple different animated actions through a single neural network model. The system learns diverse motion patterns from training data and can generate various actions (running, jumping, fighting, etc.) on demand, eliminating the need for separate animation systems for each action type.
Solution Approach 2:
The patent uses motion capture data as training copies to teach the neural network realistic human movement patterns. By learning from recorded human motions, the system can generate new animations that replicate realistic physics and biomechanics without requiring manual creation of each animation sequence.
3Loss of time
If traditional animation methods are used, then development time is reduced, but visual artifacts and monotony appear in spectating experiences
Solution Approach 1:
The patent replaces traditional keyframe animation and motion blending methods with a neural network-based system that generates animations dynamically. This substitution eliminates visual artifacts caused by poor blending and enables physically-plausible transitions that maintain visual quality while reducing manual development time.
Solution Approach 2:
The animation system serves itself by using the neural network to automatically generate appropriate animations based on current game states. The system self-adjusts transition timing and selection without manual intervention, eliminating monotony and visual artifacts while maintaining efficient development throughput.
Data Source
AI summary
A computing system may provide functionality for controlling an animated model to perform actions and to perform transitions therebetween. The system may determine, from among a plurality of edges from a first node of a control graph to respective other nodes of the control graph, a selected edge from the first control node to a selected node. The system may then determine controls for an animated model in a simulation based at least in part on the selected edge, control data associated with the selected node, a current simulation state of the simulation, and a machine learned algorithm, determine an updated simulation state of the simulation based at least in part on the controls for the animated model, and adapt one or more parameters of the machine learned algorithm based at least in part on the updated simulation state and a desired simulation state.