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We present a new approach to segment and visualize informally captured multi-stream meeting videos. We process the visual content in each stream individually by analyzing the differences between frames in each sequence to find change areas. These results are combined with face detection to determine visual activity in each of the streams. We then combine the activity scores from multiple streams and automatically generate a 3D representation of the video. Our representation allows the user to obtain an at-a-glance view of the video at different granularities of activity, view multiple streams simultaneously, and select particular points in time for viewing. We present experiments that suggest that low-level visual analysis can be effective for finding highlights that can be used for browsing multi-stream meeting videos.