Visual Computing

We propose a novel jaw rig, empirically designed from captured data, that provides more accurate jaw positioning and constrains the motion to physiologically possible poses while offering intuitive control.

In this paper, we describe our first contribution to AR Poser: a technique for digital characters to recognize and automatically reproduce the same pose as a person in a picture (using only RGB information from a mobile device).

We present a method for adding artistic control to physics-based hair simulation.

We present a novel user-guided approach to correcting these common lip shape errors present in traditional capture systems.

We propose a novel neural network architecture for point cloud classification.

Our normalized cut loss approach to segmentation brings the quality of weakly-supervised training significantly closer to fully supervised methods.

We propose a new approach, PhaseNet, that is designed to robustly handle challenging scenarios while also coping with larger motion.

We propose a method (ProSR) that is progressive both in architecture and training: the network upsamples an image in intermediate steps, while the learning process is organized from easy to hard, as is done in curriculum learning.

We present several mixed reality based remote collaboration settings by using consumer head-mounted displays, including an AR system linked with an AR system, a VR system with virtual body, a VR system without virtual body and a desktop computer.

In this paper, we propose a depth image and video codec based on block compression, that exploits typical characteristics of depth streams, drawing inspiration from S3TC texture compression and geometric wavelets.