We present a novel perceptually-motivated interactive tool for using color contrast to enhance details represented in the lightness channel of images and video. Our method lets users adjust the perceived contrast of different details by manipulating local chromaticity while preserving the original lightness of individual pixels. Inspired by the use of similar chromaticity mappings in painting, our tool effectively offers contrast along a user-selected gradient of chromaticities as additional bandwidth for representing and enhancing different details in an image. We provide an interface for our tool that closely resembles the familiar design of tonal contrast curve controls that are available in most professional image editing software. We show that our tool is effective for enhancing the perceived contrast of details without altering lightness in an image and present many examples of effects that can be achieved with our method on both images and video.

Humans are remarkably sensitive to the alignment of visual events with other stimuli, which makes synchronization one of the hardest tasks in video editing. A key observation of our work is that most of the alignment we do involves salient localizable events that occur sparsely in time. By learning how to recognize these events, we can greatly reduce the space of possible synchronizations that an editor or algorithm has to consider. Furthermore, by learning descriptors of these events that capture additional properties of visible motion, we can build active tools that adapt their notion of eventfulness to a given task as they are being used. Rather than learning an automatic solution to one specific problem, our goal is to make a much broader class of interactive alignment tasks significantly easier and less time-consuming. We show that a suitable visual event descriptor can be learned entirely from stochastically-generated synthetic video. We then demonstrate the usefulness of learned and adaptive eventfulness by integrating it in novel interactive tools for applications including audio-driven time warping of video and the extraction and application of sound effects across different videos.

We present ReCapture, a system that leverages AR-based guidance to help users capture time-lapse data with hand-held mobile devices. ReCapture works by repeatedly guiding users back to the precise location of previously captured images so they can record time-lapse videos one frame at a time without leaving their camera in the scene. Building on previous work in computational re-photography, we combine three different guidance modes to enable parallel hand-held time-lapse capture in general settings. We demonstrate the versatility of our system on a wide variety of subjects and scenes captured over a year of development and regular use, and explore different visualizations of unstructured hand-held time-lapse data.