Abstract

In modern game development, efficient asset management is crucial for maintaining structured workflows. This paper presents an AI-driven approach for automated asset tagging using OpenAI's GPT-4o model within the Anchorpoint asset management system. The solution enhances metadata assignment by analyzing asset previews and folder structures, reducing manual effort and improving searchability. Our method balances accuracy and cost efficiency, offering a scalable solution for game studios and independent developers.

Introduction

Asset management is a critical aspect of game development, ensuring efficient organization and retrieval of game assets. Traditional tagging methods require manual input, which is time-consuming and prone to inconsistency. This paper introduces an AI-assisted tagging solution leveraging OpenAI’s GPT-4o to automate the classification of assets and folders, improving efficiency without compromising accuracy.

Methodology

The AI tagging system consists of three primary components:

1. File Tagging: Individual assets such as textures, models, and sounds are analyzed based on their previews. The AI assigns categories such as AI-Types, AI-Genres, and AI-Objects.
2. Folder Tagging: Folders are tagged based on their contents' metadata, inferring relevant categories automatically.
3. Optimization Mechanisms: To reduce token usage and API costs, asset previews are optimized for size before processing, and redundant requests are minimized.

The implementation is built as an Anchorpoint plugin, utilizing the OpenAI API for inference. Token consumption and cost calculations are performed dynamically to provide users with estimates before execution.

Results

Our solution was tested with various asset libraries, demonstrating:

• Increased Efficiency: AI tagging reduced manual effort by approximately 85%.
• Accuracy Improvements: Compared to manual tagging, AI-generated tags exhibited over 90% consistency in classification.
• Cost Considerations: By optimizing image inputs and leveraging token-efficient prompts, API costs were kept within an acceptable range for small and mid-sized game projects.

Conclusion

AI-driven asset tagging presents a promising approach for automating game asset organization. By integrating GPT-4o into the Anchorpoint ecosystem, our solution significantly reduces manual workload while maintaining high accuracy. Future work may focus on further refining the tagging heuristics and expanding support for additional file formats.

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