Computational time-lapse video

We present methods for generating novel time-lapse videos that address the inherent sampling issues that arise with traditional photographic techniques. Starting with video-rate footage as input, our post-process downsamples the source material into a time-lapse video and provides user controls for retaining, removing, and resampling events. We employ two techniques for selecting and combining source frames to form the output. First, we present a nonuniform sampling method, based on dynamic programming, which optimizes the sampling of the input video to match the user's desired duration and visual objectives. We present multiple error metrics for this optimization, each resulting in different sampling characteristics. To complement the non-uniform sampling, we present the virtual shutter, a non-linear filtering technique that synthetically extends the exposure time of time-lapse frames.