Abstract:
This study presents a machine learning–based performance prediction framework that enables
3D artists and game developers to estimate rendering cost. such as frame rate, CPU/GPU usage,
memory consumption, and draw calls during the asset creation process. With the rapid growth
of the gaming and real-time graphics industry, the demand for performance optimized 3D assets
has increased significantly. However, existing tools such as Unity Profiler and Unreal Insights
are inherently reactive, providing feedback only after assets are imported into an engine, which
leads to iterative, time-consuming optimization cycles and production delays. To address this
gap, the proposed system introduces a proactive, real-time prediction approach that operates
at the modeling stage. A structured dataset of 3D asset features including polygon count, vertex
density, texture resolution, and shader complexity is combined with runtime performance
metrics collected from Unity. Using Random Forest, XGBoost, Multi-Layer Perceptron, and
Graph Convolutional Network models, the framework predicts key performance indicators with
high accuracy. Preliminary experiments show best R2 values for frame-rate prediction, while
maintaining millisecond-level inference latency suitable for interactive use. The trained model
is integrated into Blender through a plug-in and REST based service, providing instant feedback
to artists as they modify meshes, materials, and textures. A user survey indicates that
90% of participating artists perceive the tool as practically valuable for reducing optimization
effort. Overall, this work introduces a proactive surrogate model for 3D asset performance
prediction, with strong potential to reduce iteration cycles and streamline real-time content production
pipelines.
