Visual Computing

We propose “Stereohaptics”, a framework to create, record, modify, and playback rich and dynamic haptic media using audio based tools.

In this article, we conduct a series of experiments to evaluate the distinctiveness and attractiveness of human motions (face and body) and voices.

We propose a local deformation model composed of many small subspaces spatially distributed over the face.

We propose a new adaptive rendering method to improve the performance of Monte Carlo ray tracing, by reducing noise contained in rendered images while preserving high-frequency edges.

In this paper, we introduce a new direct skinning method that addresses this problem.

We address the problem of denoising Monte Carlo renderings by studying existing approaches and proposing a new algorithm that yields state-of-the-art performance on a wide range of scenes.

We present subdivision next-event estimation (SNEE) for unbiased Monte Carlo simulation of subsurface scattering.

In order to examine the relationship between stylistic elements of animated characters and the target ages of their audiences, we performed a series of qualitative and quantitative studies.

We propose the notion of semi-supervised vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot and open set recognition using a unified framework.

In this work we improve training of temporal deep models to better learn activity progression for activity detection and early detection tasks.