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.

翻译：现代游戏引擎大量使用学习型运动模型为NPC（非玩家角色）生成动画，耗费了大量计算资源。本文提出了"AI细节层次"（AI LOD）框架，该框架根据每个NPC与玩家相机之间的距离，动态调整机器学习推理精度。其核心理念类似于经典几何LOD：在差异难以感知的位置，用成本更低的近似模型替代。此处，近似模型是指低精度量化机器学习模型，而非低多边形网格。本文的贡献在于提出了AI LOD这一概念：推理时量化可作为AI驱动角色动画的LOD维度——更广泛地说，可适用于任何基于AI的运行时系统，只要其感知敏感性随上下文变化。本文以Li等人的卷积序列到序列模型作为代表性示例进行概念验证，将其预训练检查点导出为三种ONNX Runtime变体（FP32、FP16和INT8逐张量），并设计基于距离的选择器在运行时进行路由。在CMU Mocap数据集上的评估提供了初步证据，表明每个精度层级可在其指定距离范围内提供服务，且感知退化可忽略不计。这支持了更广泛的论点：距离感知型机器学习模型精度选择是AI驱动角色动画中一种可行的LOD策略。