If you have ever watched movies or television shows, you know how easy it is to tell the good characters from the bad ones. Little, however, is known "whether" or "how" computers can achieve such high-level understanding of movies. In this paper, we take the first step towards learning the relations among movie characters using visual and auditory cues. Specifically, we use support vector regression to estimate local characterization of adverseness at the scene level. Such local properties are then synthesized via statistical learning based on Gaussian processes to derive the affinity between the movie characters. Once the affinity is learned, we perform social network analysis to find communities of characters and identify the leader of each community. We experimentally demonstrate that the relations among characters can be determined with reasonable accuracy from the movie content.
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