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Action recognition in still images poses a great challenge due to (i) fewer available training data, (ii) absence of temporal information. To address the first challenge, we introduce a dataset for STill image Action Recognition (STAR), containing over $1M$ images across 50 different human body-motion action categories. UCF-STAR is the largest dataset in the literature for action recognition in still images. The key characteristics of UCF-STAR include (1) focusing on human body-motion rather than relatively static human-object interaction categories, (2) collecting images from the wild to benefit from a varied set of action representations, (3) appending multiple human-annotated labels per image rather than just the action label, and (4) inclusion of rich, structured and multi-modal set of metadata for each image. This departs from existing datasets, which typically provide single annotation in a smaller number of images and categories, with no metadata. UCF-STAR exposes the intrinsic difficulty of action recognition through its realistic scene and action complexity. To benchmark and demonstrate the benefits of UCF-STAR as a large-scale dataset, and to show the role of “latent” motion information in recognizing human actions in still images, we present a novel approach relying on predicting temporal information, yielding higher accuracy on 5 widely-used datasets.