Recently, interest in shape grammar models of pictorial style has been revived by the success of generative modelers for buildings and cities. Stochastic methods have been introduced for learning the parameters of shape grammars to adapt to different architectural or visual styles, including Mondrian. We extend those recent approaches for generating 3D graphics in the style of Keith Haring paintings, whose visual vocabulary is significantly more complex than Mondrian. We propose a four-tiered stochastic plex grammar which decomposes scenes into figures, figures into bodies, bodies into body parts, and body parts into surfaces. Each tier of the grammar is stochastic, which allows us to generate random variations of scenes while keeping the overall style of Keith Haring paintings. Because the scenes are generated as 3D graphics, we are able to animate them into generative "Keith Haring movies".