Long-take touring (LTT) shots are characterized by smooth camera motion over a long distance that seamlessly connects different views of the captured scene. These shots offer a compelling way to visualize 3D spaces. However, filming LTT shots directly is very difficult, and rendering them based on a virtual reconstruction of a scene is resource-intensive and prone to many visual artifacts. We propose Hybrid Tours , a hybrid approach to creating LTT shots that combines the capture of short clips representing potential tour segments with a custom interactive application that lets users filter and combine these segments into longer camera trajectories. We show that Hybrid Tours makes capturing LTT shots much easier than the traditional single-take approach, and that clip-based authoring and reconstruction leads to higher-fidelity results at a lower cost than common image-based rendering workflows.

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.

For modern large-scale networked systems, ranging from cloud to edge computing systems, the topology design has a significant impact on the system performance in terms of scalability, cost, latency, throughput, and fault-tolerance. These performance metrics may conflict with each other and design criteria often vary across different networks. To date, there has been little theoretic foundation on topology designs from a prescriptive perspective, indicating that the current status quo of the design process is more of an art than a science. In this paper, we advocate a novel unified framework to describe, generate, and analyze topology design in a systematic fashion. By reverse-engineering existing topology designs and developing a fine-grained decomposition method for topology design, we propose a general procedure that serves as a common language to describe topology design. By proposing general criteria for the procedure, we devise a top-down approach to generate topology models, based on which we can systematically construct and analyze new topologies. To validate our approach, we leverage concrete tools based on combinatorial design theory and propose a novel layered topology model. With quantitative performance analysis, we reveal the trade-offs among performance metrics and generate new topologies with various advantages for different large-scale networks.