Style Dial Model for Animation Parameter Control
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Solution Overview
Problem
Animation generation often requires tedious adjustments to parameter values, making it time-consuming for animators to achieve desired content styles in animation scenes.
Innovation Solution
A system that trains a style dial model to determine individual style correlation values based on user-provided style assignments and animation scene information, allowing users to adjust content styles using virtual dials or sliders for efficient generation of styled animation scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional parameter adjustment methods are used to modify content styles in animation scenes, then precise control over animation parameters can be achieved, but the process becomes time-consuming and tedious
Solution Approach 1:
The patent introduces a machine learning model as an intermediary between the user's style preferences and the animation scene parameters. The model automatically translates high-level style descriptions into specific parameter adjustments, eliminating the need for manual parameter tuning while maintaining precise control over the animation content style.
Solution Approach 2:
The system enables self-service by allowing users to simply provide style assignments without needing to understand or manually adjust complex animation parameters. The machine learning model autonomously performs the parameter optimization, making the system serve itself in translating creative intent into technical implementation.
2Adaptability or versatility
If manual adjustment of animation parameters is performed to achieve desired content styles, then customization flexibility is maintained, but operational complexity increases
Solution Approach 1:
The patent uses a machine learning model trained on examples of style assignments and corresponding animation parameters. The model learns to copy successful parameter configurations from training data and applies them to new style requests, maintaining customization flexibility while simplifying the user operation to simple style descriptions.
3Adaptability or versatility
If extensive parameter reconfiguration is performed to change content styles, then animation scene adaptability improves, but productivity decreases
Solution Approach 1:
The machine learning model is pre-trained on a comprehensive dataset of style assignments and animation parameters before deployment. This preliminary training action enables the model to quickly adapt to various style requirements during actual use without requiring extensive real-time parameter reconfiguration, thus maintaining high productivity while achieving scene adaptability.
Data Source
AI summary
Systems and methods to generate content styles for animation, and to adjust content styles by adjusting virtual style sliders for the content styles are disclosed. Exemplary implementations may: receive, from client computing platforms associated with users, style assignments for final compiled animation scenes; provide a style dial model with the style assignments and the corresponding final compiled animation scenes; train, from i) the style assignments, ii) the corresponding final compiled animation scenes, and iii) animation scene information that defines the final compiled animation scenes, the style dial model to determine individual style correlation values; receive user entry of levels of correlation to individual ones of multiple different content styles; input the animation scene and the levels of correlation to a model that outputs adjusted animation scene information that defines a styled compiled animation scene that has the selected levels of correlation to the individual content styles.