Visual Computing

This paper discusses the benefits and tradeoffs between agent-centric and event-centric approaches towards authoring the domain knowledge of story worlds.

We present a method to automatically convert videos and CG animations to stylized animated line-drawings.

We present six prototype applications that explore and develop Augmented Creativity in different ways, cultivating creativity through AR interactivity.

This paper applies the algebraic multigrid method known as smoothed aggregation to cloth simulation.

We propose a simple analysis model for view sampling and an adaptive, online sampling algorithm tailored to light field depth reconstruction.

We propose a new adaptive rendering algorithm that enhances the performance of Monte Carlo ray tracing by reducing the noise, i.e., variance while preserving a variety of high-frequency edges in rendered images through a novel prediction based reconstruction.

We present a novel, sampling-based framework for processing video that enables high-quality scene-space video effects in the presence of inevitable errors in depth and camera pose estimation.

We present a perceptual control space for simulation of cloth that works with any physical simulator, treating it as a black box.

We address the problem of modeling and rendering granular materials—such as large structures made of sand, snow, or sugar— where an aggregate object is composed of many randomly oriented, but discernible grains.

This paper introduces “OmniAD,” a novel data-driven pipeline to model and acquire the aerodynamics of three-dimensional rigid objects.