Language can describe our visual world at many levels, including not only what is literally there but also the sentiment that it invokes. In this paper, we study visual language, both literal and sentimental, that describes the overall appearance and style of virtual characters. Sentimental properties, including labels such as “youthful ” or “country western,” must be inferred from descriptions of the more literal properties, such as facial features and clothing selection. We present a new dataset, collected to describe Xbox avatars, as well as models for learning the relationships between these avatars and their literal and sentimental descriptions. In a series of experiments, we demonstrate that such learned models can be used for a range of tasks, including predicting sentimental words and using them to rank and build avatars. Together, these results demonstrate that sentimental language provides a concise (though noisy) means of specifying low-level visual properties.
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