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arXiv · 2408.04068

Digital Avatars: Framework Development and Their Evaluation

Timothy Rupprecht·Sung-En Chang·Yushu Wu·Lei Lu·Enfu Nan·Chih-hsiang Li·Caiyue Lai·Zhimin Li·Zhijun Hu·Yumei He·David Kaeli·Yanzhi Wang
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Abstract

We present a novel prompting strategy for artificial intelligence driven digital avatars. To better quantify how our prompting strategy affects anthropomorphic features like humor, authenticity, and favorability we present Crowd Vote - an adaptation of Crowd Score that allows for judges to elect a large language model (LLM) candidate over competitors answering the same or similar prompts. To visualize the responses of our LLM, and the effectiveness of our prompting strategy we propose an end-to-end framework for creating high-fidelity artificial intelligence (AI) driven digital avatars. This pipeline effectively captures an individual's essence for interaction and our streaming algorithm delivers a high-quality digital avatar with real-time audio-video streaming from server to mobile device. Both our visualization tool, and our Crowd Vote metrics demonstrate our AI driven digital avatars have state-of-the-art humor, authenticity, and favorability outperforming all competitors and baselines. In the case of our Donald Trump and Joe Biden avatars, their authenticity and favorability are rated higher than even their real-world equivalents.

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Timothy Rupprecht, Sung-En Chang, Yushu Wu, Lei Lu, Enfu Nan, Chih-hsiang Li, Caiyue Lai, Zhimin Li, Zhijun Hu, Yumei He, David Kaeli, Yanzhi Wang. 2024-08-07. Digital Avatars: Framework Development and Their Evaluation. https://doi.org/10.24963/ijcai.2024/1031

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