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Existing language and vision models achieve impressive performance inimage-text understanding. Yet, it is an open question to what extent they canbe used for language understanding in 3D environments and whether theyimplicitly acquire 3D object knowledge, e.g. about different views of anobject. In this paper, we investigate whether a state-of-the-art language andvision model, CLIP, is able to ground perspective descriptions of a 3D objectand identify canonical views of common objects based on text queries. Wepresent an evaluation framework that uses a circling camera around a 3D objectto generate images from different viewpoints and evaluate them in terms oftheir similarity to natural language descriptions. We find that a pre-trainedCLIP model performs poorly on most canonical views and that fine-tuning usinghard negative sampling and random contrasting yields good results even underconditions with little available training data.

Paparazzi: A Deep Dive into the Capabilities of Language and Vision Models for Grounding Viewpoint Descriptions