AVATAR-AGENT: A Multi-Agent System for Expressive 3D Avatar Generation
Empowering Intuitive 3D Avatar Creation with LLM-Powered Multi-Agent Systems
AVATAR-AGENT revolutionizes digital identity creation by enabling users to generate diverse, high-quality 3D avatars from natural language prompts. Our system leverages hierarchical planning, intelligent asset retrieval, and iterative self-correction within a multi-agent architecture to ensure platform compatibility and creator economy alignment.
Key Advancements in Agentic Avatar Generation
Our research introduces a novel agentic retrieval-based paradigm for avatar generation, achieving high constraint satisfaction (94.0%) and human-comparable quality. This approach sidesteps generative model challenges by intelligently composing avatars from existing creator-made assets, preserving aesthetic consistency and supporting a vibrant content ecosystem. Iterative refinement and short-term memory are proven critical for superior outcomes.
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Prompt Constraint Satisfaction
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Average Avatar Quality Score
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Avg. Iterations for Success
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Abstract
2. Formalizing Agentic Avatar Generation
5. Experimental Evaluation
Abstract
This paper presents AVATAR-AGENT, a multi-agent system that applies hierarchical planning, tool use, and iterative refinement to avatar generation from natural language. By decomposing generation into specialized phases (intent understanding, strategic planning, execution, and validation) and using retrieval-and-assembly over existing creator assets, the system achieves high constraint satisfaction while producing quality comparable to avatars created by humans. The principles demonstrated here (hierarchical decomposition, memory-driven adaptive search, and targeted self-correction) extend beyond avatar creation to any domain requiring compositional assembly of discrete components to satisfy nuanced specifications. These results suggest several promising directions for future work:
2. Formalizing Agentic Avatar Generation
We formalize AVATAR-AGENT as a Multi-Agent System (MAS) with three core components: a set of specialized agents (A), a shared environment (E) containing external resources, and an interaction protocol (II) governing communication. This provides a rigorous foundation for understanding how LLM-powered agents collaborate to generate avatars from natural language.
AVATAR-AGENT System Overview
User Prompt
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Input & Understanding
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Strategic Planning
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Task Decomposition (Sub-Planner)
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Tool Execution (Executor)
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Verification & Quality Assessment
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Generated Avatar
5. Experimental Evaluation
We evaluate AVATAR-AGENT on 100 diverse synthetically generated prompts and address three research questions, finding that: (RQ1) The hierarchical planning approach achieves high constraint satisfaction, substantially outperforming prior retrieval-based methods. (RQ2) Both iterative refinement and short-term memory provide significant improvements to constraint satisfaction and quality. (RQ3) Automated generation achieves quality statistically comparable to avatars created by humans.
| Performance Comparison: AVATAR-AGENT vs. Baselines | |||
|---|---|---|---|
| Approach | Constraint Satisfaction | Quality Score | Avg. Iterations |
| AVATAR-AGENT | 94.0% | 0.949 | 1.96 |
| No Memory | 92.0% | 0.925 | 2.25 |
| No Iteration | 83.5% | 0.888 | - |
| Semantic Retrieval | 72.6% | 0.906 | - |
| Human-Created Avatars | - | 0.961 | - |
94.0%
Prompt Constraint Satisfaction Rate
AVATAR-AGENT significantly outperforms baselines, ensuring user requirements are explicitly tracked and verified through structured action sequences and multi-stage validation.
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