Estimating human body measurements from monocular RGB imagery remains challenging due to scale ambiguity, viewpoint sensitivity, and the absence of explicit depth information. This work presents a systematic empirical study of three weakly calibrated monocular strategies: landmark-based geometry, pose-driven regression, and object-calibrated silhouettes, evaluated under semi-constrained conditions using consumer-grade cameras. Rather than pursuing state-of-the-art accuracy, the study analyzes how differing calibration assumptions influence measurement behavior, robustness, and failure modes across varied body types. The results reveal a clear trade-off between user effort during calibration and the stability of resulting circumferential quantities. This paper serves as an empirical design reference for lightweight monocular human measurement systems intended for deployment on consumer devices.

翻译：从单目RGB图像估计人体尺寸仍具有挑战性，主要源于尺度模糊性、视角敏感性以及显式深度信息的缺失。本研究对三种弱标定单目策略进行了系统的实证分析：基于关键点的几何方法、姿态驱动的回归方法以及物体标定的轮廓方法，并在半约束条件下使用消费级相机进行了评估。研究并非追求最先进的精度，而是重点分析不同的标定假设如何影响不同体型下的测量行为、鲁棒性及失效模式。结果表明，标定过程中的用户投入与所得围度量的稳定性之间存在明显的权衡关系。本文旨在为面向消费级设备部署的轻量级单目人体测量系统提供实证设计参考。