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Title An Improved Text-To-Gesture Generation Model Based On A Hybrid Deep Learning Approach

Author(s) Tehmeema Irfan

Abstract Non-verbal cues play a pivotal role in providing human-like interaction with Artificial Intelligent Machines like Robots and Digital Assistants. The goal to give machines a human-like aptness is under research for years. Previous work to generate gestures is mostly from speech and also those models are subject to limitations like speaker dependency and gesture quality. This research is to target such problems and develop a gesture-generation model that is capable of producing quality gestures against a sequence of text input words independent of any speaker. Altering an existing specific speaker dataset, a new dataset is prepared including words and gestures. Integration of a sequential Long Short Term Memory algorithm in the model has improved the accuracy of the gestures in terms of Percentage of Corrected Key Points. The experiment results and comparison with relevant schemes show the achievement of the proposed model as it has maximized the PCK and minimized the error rate.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Computer Science

Language English

Publication Date 2023-12-18

Subject Computer Science

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