AgentAvatar: Disentangling Planning, Driving and Rendering for Photorealistic Avatar Agents

• URL: http://arxiv.org/abs/2311.17465v3
• Date: Mon, 4 Dec 2023 16:49:18 GMT
• Title: AgentAvatar: Disentangling Planning, Driving and Rendering for Photorealistic Avatar Agents
• Authors: Duomin Wang, Bin Dai, Yu Deng, Baoyuan Wang
• Abstract summary: Our framework harnesses LLMs to produce a series of detailed text descriptions of the avatar agents' facial motions. These descriptions are processed by our task-agnostic driving engine into continuous motion embeddings. Our framework adapts to a variety of non-verbal avatar interactions, both monadic and dyadic.
• Score: 16.544688997764293
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this study, our goal is to create interactive avatar agents that can autonomously plan and animate nuanced facial movements realistically, from both visual and behavioral perspectives. Given high-level inputs about the environment and agent profile, our framework harnesses LLMs to produce a series of detailed text descriptions of the avatar agents' facial motions. These descriptions are then processed by our task-agnostic driving engine into motion token sequences, which are subsequently converted into continuous motion embeddings that are further consumed by our standalone neural-based renderer to generate the final photorealistic avatar animations. These streamlined processes allow our framework to adapt to a variety of non-verbal avatar interactions, both monadic and dyadic. Our extensive study, which includes experiments on both newly compiled and existing datasets featuring two types of agents -- one capable of monadic interaction with the environment, and the other designed for dyadic conversation -- validates the effectiveness and versatility of our approach. To our knowledge, we advanced a leap step by combining LLMs and neural rendering for generalized non-verbal prediction and photo-realistic rendering of avatar agents.

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