Visual Computing

In this paper, we present the first method for *stylized* hair capture, a technique to reconstruct an individual’s actual hair-style in a manner suitable for physical reproduction.

We present an approach that takes multiple videos captured by social cameras that are carried or worn by members of the group involved in an activity and produces a coherent “cut” video of the activity.

In this project, we develop the first automatic face stabilization method that achieves professional-quality results on large sets of facial expressions.

We develop a novel, comprehensive theory of volumetric radiance estimation that leads to several new insights and includes all previously published estimates as special cases.

In this work, we focus on voice synchronization, an aspect of video quality that is strongly affected by current post-production and transmission practices. We examined the synchronization of an actor’s voice and lip movements in two distinct scenarios.

We propose a composite Dual-Frequency Continuous-Wave and Continuous- Wave (DFCW/CW) radar system for localization of backscatter RF tags.

This paper describes a method for scene reconstruction of complex, detailed environments from 3D light fields. Densely sampled light fields in the order of 109 light rays allow us to capture the real world in unparalleled detail, but efficiently processing this amount of data to generate an equally detailed reconstruction represents a significant challenge to existing algorithms.

This project develops a passive stereo system for capturing the 3D geometry of a face in a single-shot with standard light sources.

We propose a passive, camera-based system that is robust against arbitrary motion since all data is acquired within the time period of a single exposure.

This paper addresses the problem of remapping the disparity range of stereoscopic images and video.