Digital Avatars: Framework Development and Their Evaluation

TL;DR

This paper introduces a new framework for creating high-fidelity AI-driven digital avatars with a novel prompting strategy and evaluation metrics, demonstrating superior humor, authenticity, and favorability compared to competitors.

Contribution

It develops an end-to-end pipeline for real-time digital avatar creation and introduces Crowd Vote for effective evaluation of anthropomorphic features.

Findings

Avatars outperform competitors in humor, authenticity, and favorability.

Crowd Vote effectively quantifies avatar qualities.

Real-time streaming delivers high-quality digital avatars.

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.