Abstract: We introduce indirect shape analysis, or ISA, where a given shape is analyzed not based on geometric or topological features computed directly from the shape itself, but by studying how external agents interact with the shape. The potential benefits of ISA are two-fold. First, agent–object interactions often reveal an object׳s function, which plays a key role in shape understanding. Second, compared to direct shape analysis, ISA, which utilizes pre-selected agents, is less affected by imperfections of, or inconsistencies between, the geometry or topology of the analyzed shapes. We employ digital human models as the external agents and develop a prototype ISA scheme for 3D shape classification and retrieval. Given a 3D model M, we compute an ISA feature vector for M by encoding how well a selected set of human models, with functional poses, can be aligned to M so as to perform the intended functions. We demonstrate the discriminability of ISA features for 3D shape retrieval and compare to state-of-the-art methods.