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Recovering a person's height from a single image is important for virtualgarment fitting, autonomous driving and surveillance, however, it is also verychallenging due to the absence of absolute scale information. We tackle therarely addressed case, where camera parameters and scene geometry is unknown.To nevertheless resolve the inherent scale ambiguity, we infer height fromstatistics that are intrinsic to human anatomy and can be estimated from imagesdirectly, such as articulated pose, bone length proportions, and facialfeatures. Our contribution is twofold. First, we experiment with differentmachine learning models to capture the relation between image content and humanheight. Second, we show that performance is predominantly limited by datasetsize and create a new dataset that is three magnitudes larger, by miningexplicit height labels and propagating them to additional images through facerecognition and assignment consistency. Our evaluation shows that monocularheight estimation is possible with a MAE of 5.56cm.

What Face and Body Shapes Can Tell About Height