We introduce a new concept for detecting the saliency of 3-D shapes, that is, human-centered saliency (HCS) detection on the surface of shapes, whereby a given shape is analyzed not based on geometric or topological features directly obtained from the shape itself, but by studying how a human uses the object. Using virtual agents to simulate the ways in which humans interact with objects helps to understand shapes and detect their salient parts in relation to their functions. HCS detection is less affected by inconsistencies between the geometry or topology of the analyzed 3-D shapes. The potential benefit of the proposed method is that it is adaptable to variable shapes with the same semantics, as well as being robust against a geometrical and topological noise. Given a 3-D shape, its salient part is detected by automatically selecting a corresponding agent and making them interact with each other. Their adaption and alignment depend on an optimization framework and a training process. We demonstrate the detected salient parts for different types of objects together with the stability thereof. The salient parts can be used for important vision tasks, such as 3-D shape retrieval.