Visual Computing

We present a method to acquire dynamic properties of facial skin appearance, including dynamic diffuse albedo encoding blood flow, dynamic specular intensity, and per-frame high-resolution normal maps for a facial performance sequence.

We propose a new interaction metaphor that reduces the gap between physical toys and virtual characters.

We present a practical solution to these artifacts which requires minimal costume parameterization work, and a straightforward inpainting approach.

The paper describes the overall workflow and detailed algorithm of each component, followed by an evaluation validating the proposed method.

In this paper, we solve this problem by combining a window-based breakdown of the controls on the temporal dimension, together with a global wide search strategy that keeps locally sub-optimal samples throughout the optimization.

This paper proposes and experimentally compares different losses integrating MRF/CRF regularization terms.

We present a novel algorithm to denoise deep Monte Carlo renderings, in which pixels contain multiple color values, each for a different range of depths.

In this work we propose a deep learning framework for color propagation that combines a local strategy, to propagate colors frame-by-frame ensuring temporal stability, and a global strategy, using semantics for color propagation within a longer range.

In this paper, we describe a complete pipeline for the capture and display of real-world Virtual Reality video content, based on the concept of omnistereoscopic panoramas.

We present a modular convolutional architecture for denoising rendered images.