Player-Edited Character Animation With ML Pose Extrapolation
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Solution Overview
Problem
Creating realistic and customizable character animations in video games is time-consuming and requires substantial manual effort, especially when dealing with complex movements and variations, which traditional blending and layering techniques often result in unnatural configurations.
Innovation Solution
A motion generation machine learning model, utilizing a deep learning framework, allows users to modify character poses and motions through an animation-editing interface, enabling realistic extrapolation of edits across various animations, including locomotion and asynchronous motions, without the need for manual intervention by animators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional hand-tuned techniques are used to create realistic character movements, then animation realism is improved, but development time and complexity increase substantially
Solution Approach 1:
The patent replaces manual hand-tuning mechanisms with an automated machine learning system. The motion generation model automatically creates and adjusts character animations based on game state inputs, eliminating the need for manual animator intervention while maintaining realistic movement quality.
Solution Approach 2:
The system enables self-service animation generation where the game application automatically generates appropriate character movements based on predefined motion rules and machine learning models. The system serves itself by automatically selecting, blending, and adjusting animations without requiring external animator input for each movement scenario.
2Adaptability or versatility
If traditional blending and layering techniques are used to create character animations, then movement variety is improved, but naturalness of movements deteriorates due to unnatural configurations
Solution Approach 1:
The patent implements dynamic animation generation where the system continuously adapts character movements based on real-time game state. The machine learning model dynamically selects and blends motion segments to create natural-looking transitions, avoiding the rigid unnatural configurations that occur with traditional static blending techniques.
Solution Approach 2:
The system changes animation parameters dynamically based on game context. The motion generation model adjusts movement parameters such as speed, direction, and body posture in real-time to maintain naturalness while providing movement variety. This allows seamless transitions between different movement types without the unnatural artifacts of traditional layering.
3Manufacturing precision
If extensive manual tuning of skeleton objects is performed to achieve realistic movements, then animation quality is improved, but ease of manufacture deteriorates
Solution Approach 1:
The patent replaces manual skeleton tuning with an automated machine learning-based motion generation system. The model automatically adjusts skeleton object positions and configurations based on desired movement types, eliminating the need for manual animator intervention while maintaining high animation quality.
Solution Approach 2:
The system introduces a machine learning model as an intermediary between the desired movement outcome and the actual character animation. This intermediary automatically handles the complex task of adjusting skeleton configurations, serving as a bridge that translates high-level movement intentions into detailed anatomical adjustments without manual intervention.
Data Source
AI summary
System and methods for using a deep learning framework to customize animation of an in-game character of a video game. The system can be preconfigured with animation rule sets corresponding to various animations. Each animation can be comprised of a series of distinct poses that collectively form the particular animation. The system can provide an animation-editing interface that enables a user of the video game to make modifications to at least one pose or frame of the animation. The system can realistically extrapolate these modifications across some or all portions of the animation. In addition or alternatively, the system can realistically extrapolate the modifications across other types of animations.