AI Level of Detail: Distance-Aware ML Model Precision Selection for Real-Time Human Motion Prediction in Games

Abstract

Modern game engines spend significant compute animating NPCs with learned motion models. This paper proposes AI Level of Detail (AI LOD), a framework in which machine learning inference precision is adapted based on the distance between each NPC and the player camera. The core idea mirrors classical geometry LOD: substitute a cheaper approximation where the difference is imperceptible. Here, the approximation is a lower-precision quantized machine learning model rather than a lower-polygon mesh. The contribution of this work is the AI LOD concept itself: that inference-time quantization can serve as the LOD axis for AI-driven character animation - and more broadly, for any AI-based runtime system where perceptual sensitivity varies with context. The convolutional sequence-to-sequence model of Li et al. is used as a representative example to demonstrate the concept, with its trained checkpoint exported into three ONNX Runtime variants (FP32, FP16, and INT8 per-tensor), intended to be routed by a distance-based selector at runtime. Evaluation on the CMU Mocap dataset provides initial evidence that each precision tier can be served at its assigned distance range with negligible perceptible degradation, supporting the broader premise that distance-aware ML model precision selection is a viable LOD strategy for AI-based character animation.