Text-to-Animation Rendering via Semantic Template Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current technologies lack the ability to automatically convert natural language text into animated clips that logically correspond to the text, especially when limited to specific vocabularies or topics, including colloquial expressions.
Innovation Solution
A method that processes natural language text by dividing it into sentences, normalizing words, selecting and combining templates, and matching these with animation files to create an animated clip, which includes features like misprint correction, style assignment, and cache-based optimization for efficient animation generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual drawing methods are used to create animated clips, then customization and creativity are improved, but time consumption and labor intensity increase significantly
Solution Approach 1:
The patent segments the text into sentences, words, and semantic units that can be independently matched with animation templates. This segmentation enables automated processing of each unit through template matching, eliminating the need for manual frame-by-frame drawing while maintaining customization capabilities through semantic-based template selection.
Solution Approach 2:
The patent creates a library of pre-defined animation templates that have been prepared in advance for various semantic units and emotions. These templates are stored and can be automatically selected and combined based on the input text, eliminating the need for real-time manual creation while preserving artistic quality through pre-designed animations.
2Productivity
If automated text-to-image conversion is implemented, then productivity is improved, but semantic accuracy and contextual understanding deteriorate
Solution Approach 1:
The patent introduces semantic templates as an intermediary layer between the input text and the animation output. The text is first processed to identify semantic units, which then match with predefined templates containing emotional and contextual information. This intermediary layer enables automated processing while maintaining semantic accuracy through the structured template matching process.
Solution Approach 2:
The patent transforms the text into normalized semantic parameters that can be systematically matched with animation templates. By converting natural language into structured semantic units with defined properties (emotions, actions, objects), the system achieves both automation efficiency and semantic precision through parameter-based matching rather than direct text-to-animation conversion.
3Device complexity
If a limited vocabulary and topic set are used in automated animation, then system complexity is reduced, but language coverage and applicability decrease
Solution Approach 1:
The patent creates universal animation templates that can serve multiple functions across different contexts. Each template is designed to represent fundamental semantic units (emotions, actions, objects) that can be combined in various ways to cover a wide range of languages and topics. This universality allows the system to maintain low complexity while achieving high adaptability through combinatorial template usage.
Solution Approach 2:
The patent uses parameter-based template matching where templates are defined by semantic parameters rather than specific vocabulary. This allows the same template structure to accommodate different words and topics across languages by changing the parameter values, thereby expanding language coverage without increasing system complexity.
Data Source
AI summary
The present invention relates to rendering texts in a natural language, namely to manipulating a text in a natural language to generate an image or animation corresponding to this text. The invention is unique mainly in that a sequence of animations is selected, semantically corresponding to a given text. Given a set of animations and a text, the invention makes it possible to compare the sequence of these animations to this text. It is unique in that text templates are used and an optimum sequence of these templates is determined. The idea of the template-based text rendering consists in that the text is manipulated to generate an image or animation with the aid of searching correspondences to a limited number of predefined templates. An animation according to certain style is selected in compliance with each template. Animations are sequentially combined into a single sequence of video images.