Glyph Stroke Animation Using AI-Derived Brush Mapping

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Solution Overview

Problem

Conventional methods for animating text in digital video editing are laborious, time-consuming, and prone to errors, especially when dealing with variable fonts, and relying solely on generative AI fails to capture typographic nuances.

Innovation Solution

A machine-learning model is used to determine centerlines and continuous mappings of brush head shapes for letter glyphs, integrating a robust metadata input system to generate animated realizations of text, allowing for non-destructive editing and seamless animations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional masking and animation methods are used, then text animation can be achieved, but the process is laborious and time-consuming

Engineering Contradiction:

Improvetext animation efficiencyVSAvoidtime required for text animation

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual masking and animation operations with an AI-based system that automatically generates text animations. The AI model processes text input and directly produces animated visualizations, eliminating the need for manual frame-by-frame masking and animation creation, thus dramatically improving productivity and reducing time investment.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service text animation generation where the AI model autonomously creates animations without requiring manual intervention. Users simply provide text input, and the system automatically handles all animation generation tasks including mask creation, animation sequencing, and rendering, making the process efficient and accessible.

Inventive Principle:

Principle #25Self-service

2Ease of operation

If manual masking and animation is performed, then text animation is achieved, but it requires significant manual effort

Engineering Contradiction:

Improveease of text animation creationVSAvoidcomplexity of animation process

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The patent replaces complex manual animation procedures with an AI-driven automatic generation system. The AI model handles all aspects of animation creation including generating appropriate masks, determining animation sequences, and rendering final outputs, thereby simplifying the user interface and making text animation accessible to users without specialized animation skills.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Adaptability or versatility

If variable fonts are animated using conventional methods, then font variations can be displayed, but the process becomes even more time-consuming

Engineering Contradiction:

Improvefont variation capabilityVSAvoidanimation creation speed

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent implements dynamic font animation by training the AI model to understand and generate animations for variable fonts. The system can handle font variations including weight, width, and style changes by learning from training data that includes diverse font examples, enabling smooth transitions between different font states without requiring separate manual animation processes for each variation.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The AI-based system provides universal text animation capability that works across different font types and styles. The model is trained to handle various font characteristics and can generate appropriate animations for any font variation, making it adaptable to diverse typography needs while maintaining consistent performance and productivity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Extent of automation

If generative AI is used alone, then automation is achieved, but it fails to capture typographic nuances

Engineering Contradiction:

Improveautomation of text animationVSAvoidtypographic accuracy

Core Design Contradiction:

Extent of automationVSManufacturing precision

Solution Approach 1:

The patent applies local quality by training the AI model on specific typographic data and using conditional inputs that preserve font-specific characteristics. The system processes text with attention to local typographic features such as letter spacing, kerning, and font-specific styling, ensuring that automated generation maintains high typographic accuracy while achieving full automation.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system incorporates feedback mechanisms where the AI model's outputs are evaluated against ground truth data during training and can be refined through iterative optimization. This feedback loop ensures that the automated system learns to capture typographic nuances accurately, improving both automation capability and typographic precision simultaneously.

Inventive Principle:

Principle #23Feedback

Data Source

PatentEP4708116A1System and method for customized text animation

Publication Date: 2026.03.11 FIGMA INC

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AI summary

Systems or methods for presenting or generating an animated realization of one or more letter glyphs based on progressively exposing a continuous mapping of brush head shapes following a letterform stroke order. A machine-learning model is used to generate animation metadata from the one or more letter glyphs. The animation metadata including a centerline, the continuous mapping of brush head shapes that is used to sweep along the centerline, and the letterform stroke order to generate the animated realization of the one or more letter glyphs.