Real-time scene gesture generation method and device and digital human interaction system
By using the action generation model and semantic enhancement dataset to generate semantic gesture actions in the digital human real-time dialogue interaction scenario, the problem of inadequate natural and accurate gesture expression in the prior art is solved, and the unified expression of semantic content and gesture content of language text is realized.
CN119648875APending Publication Date: 2025-03-18BEIJING KNOWLEDGE ATLAS TECHNOLOGY CO LTD
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Patent Information
- Application Number
- CN202411693754.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-25
- Publication Date
- 2025-03-18
AI Technical Summary
Technical Problem
The prior art is difficult to generate semantic gesture actions in real-time dialogue interaction scenarios for digital people, resulting in inadequate gesture expression.
Method used
By obtaining the input text, the action generation model is used to generate basic gesture actions, and the corresponding index semantic actions are retrieved from the pre-established semantic augmentation dataset, and the digital gesture actions are generated by superimposing index semantic actions.
Benefits of technology
It realizes the unity of semantic content and gesture content expression of language text while ensuring real-time effects, enhances the semantic effect of digital human gesture expression, and improves the naturalness and accuracy of interactive experience.
✦ Generated by Eureka AI based on patent content.
Abstract
The invention relates to a real-time scene gesture generation method and device and a digital human interaction system, and belongs to the technical field of artificial intelligence. The method comprises the steps of obtaining an input text; generating a basic gesture action of the digital human based on the input text by adopting an action generation model; adopting a similarity matching method to retrieve an index semantic action corresponding to the input text from a pre-established semantic enhancement data set; and superposing the index semantic action into the sequence of the basic gesture actions to generate the gesture action of the digital human. According to the method, a large model technology is introduced, accurate semantic text descriptions of semantic gestures are generated in batches, retrieval matching is carried out through word vector similarity calculation, and index semantic actions are superposed in the generation process of a speaking digital person, so that the semantic effect expressed by the gestures of the digital person is enhanced, and the accuracy of the speech digital person is improved. Unification of language text semantic content and gesture content expression is achieved on the premise that the real-time effect is guaranteed.
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