Automatic effect generation
AssigneeLemon Inc.
InventorsJie Meng, Jagminder Shergill, Shih-Kuang Chu, Diego Escala, Jiayue Zhu
Life of this patent
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Abstract
The present disclosure describes techniques for automatically generating effects. A plurality of effect ideas may be generated by at least one trained machine learning model in response to receiving text input by a user. An effect idea may be decomposed into a plurality of components in response to selecting the effect idea. The effect idea may be among the plurality of effect ideas. Executable code may be generated based on decomposing the effect idea. The code may be executed by a predetermined effect creation tool to generate an effect. The generated effect may be output.
Claims20
A method of automatically generating effects, comprising:
- generating a plurality of effect ideas by at least one trained machine learning model in response to receiving text input;
- decomposing an effect idea into a plurality of components in response to selecting the effect idea, wherein the effect idea is among the plurality of effect ideas;
- generating executable code based on decomposing the effect idea;
- executing the code by a predetermined effect creation tool to generate an effect;
- and
- output the generated effect.
A system, comprising:
- at least one processor;
- and
- at least one memory comprising computer-readable instructions that upon execution by the at least one processor cause the system to perform operations comprising:
- generating a plurality of effect ideas by at least one trained machine learning model in response to receiving text input;
- decomposing an effect idea into a plurality of components in response to selecting the effect idea, wherein the effect idea is among the plurality of effect ideas;
- generating executable code based on decomposing the effect idea;
- and
- executing the code by a predetermined effect creation tool to generate an effect;
- and
- output the generated effect.
A non-transitory computer-readable storage medium, storing computer-readable instructions that upon execution by a processor cause the processor to implement operations, the operation comprising:
- generating a plurality of effect ideas by at least one trained machine learning model in response to receiving text input;
- decomposing an effect idea into a plurality of components in response to selecting the effect idea, wherein the effect idea is among the plurality of effect ideas;
- generating executable code based on decomposing the effect idea;
- and
- executing the code by a predetermined effect creation tool to generate an effect;
- and
- output the generated effect.
Dependencies are inferred from the claim text.