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Augmented Reality Based Recommendations Based on Perceptual Shape Style Compatibility with Objects in the Viewpoint and Color Compatibility with the Background

Augmented Reality (AR) has been heralded as the next frontier in retail, but so far, has been mostly used to advertise or market products in a gimmicky way and its true potential in digital marketing remains unexploited. In this work, we leverage richer data coming from AR usage to make re-targeting much more persuasive via viewpoint image augmentation. Based on the user's purchase viewpoint visual, we identify relevant objects/products present in the viewpoint along with their style such that products with more style compatibility with those surrounding real-world objects can be recommended. We also use color compatibility with the background of the user's purchase viewpoint to select suitable product textures. We embed the recommended products in the viewpoint at the location of the initially browsed product with similar pose and scale. This makes the recommendations much more personalized and relevant which can increase conversions. Evaluation with user studies show that our system is able to make recommendations better than tag-based recommendations, and targeting using the viewpoint is better than that of usual product catalogs.