I first present a series of older projects in Video Indexing, starting with the DIVAN project at INA and the MPEG expert group (1998-2000), and continuing at INRIA under the VIBES project (2001-2004). This research falls under the general approach of “computational media aesthetics”, where we attempt to recognize film events based on our knowledge of filming styles and conventions (cinematography and editing). This is illustrated with two applications – the automatic segmentation of TV news into topics; and the automatic indexing of movies with their scripts.

I then present my more recent research in Action Recognition with the MOVI group at INRIA (2005-2008). Building upon the GRIMAGE infrastructure, I present experiments in (1) learning and recognizing a small repertoire of full body gestures in 3D using “motion history volumes”; (2) segmenting a raw stream of 3D image sequences into recognizable “primitive actions” ; and (3) using statistical models learned in 3D for recovering primitive actions and relative camera positions from a single 2D video recording of similar actions.

Finally, I propose a road map for reconciling those two research topics, by simultaneously learning statistical models of human action and filming styles from large collections of annotated movies. I illustrate this new research agenda with novel applications in interactive storytelling, video games and machinima.