Research Area
Year Published

103 Results

July 30, 2018

Instant 3D Photography


We present an algorithm for constructing 3D panoramas from a sequence of aligned color-and-depth image pairs. Such sequences can be conveniently captured using dual lens cell phone cameras that reconstruct depth maps from synchronized stereo image capture.

By: Peter Hedman, Johannes Kopf

July 29, 2018

Online Optical Marker-based Hand Tracking with Deep Labels

Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH)

We propose a technique that frames the labeling problem as a keypoint regression problem conducive to a solution using convolutional neural networks.

By: Shangchen Han, Beibei Liu, Robert Wang, Yuting Ye, Christopher D. Twigg, Kenrick Kin

July 7, 2018

Reconstructing Scenes with Mirror and Glass Surfaces


We introduce a fully automatic pipeline that allows us to reconstruct the geometry and extent of planar glass and mirror surfaces while being able to distinguish between the two.

By: Thomas Whelan, Michael Goesele, Steven J. Lovegrove, Julian Straub, Simon Green, Richard Szeliski, Steven Butterfield, Shobhit Verma, Richard Newcombe

July 6, 2018

DeepFocus: Learned Image Synthesis for Computational Display

ACM SIGGRAPH (Talks Program)

In this paper, we introduce Deep-Focus, a generic, end-to-end trainable convolutional neural network designed to efficiently solve the full range of computational tasks for accommodation-supporting HMDs.

By: Lei Xiao, Anton Kaplanyan, Alexander Fix, Matt Chapman, Douglas Lanman

June 18, 2018

Modeling Facial Geometry using Compositional VAEs

Computer Vision and Pattern Recognition (CVPR)

We propose a method for learning non-linear face geometry representations using deep generative models. Our model is a variational autoencoder with multiple levels of hidden variables where lower layers capture global geometry and higher ones encode more local deformations.

By: Timur Bagautdinov, Chenglei Wu, Jason Saragih, Pascal Fua, Yaser Sheikh

June 18, 2018

Eye In-Painting with Exemplar Generative Adversarial Networks

Computer Vision and Pattern Recognition (CVPR)

This paper introduces a novel approach to in-painting where the identity of the object to remove or change is preserved and accounted for at inference time: Exemplar GANs (ExGANs). ExGANs are a type of conditional GAN that utilize exemplar information to produce high-quality, personalized in-painting results.

By: Brian Dolhansky, Cristian Canton Ferrer

June 18, 2018

Total Capture: A 3D Deformation Model for Tracking Faces, Hands, and Bodies

Computer Vision and Pattern Recognition (CVPR)

We present a unified deformation model for the markerless capture of human movement at multiple scales, including facial expressions, body motion, and hand gestures.

By: Hanbyul Joo, Tomas Simon, Yaser Sheikh

June 18, 2018

Audio to Body Dynamics

Computer Vision and Pattern Recognition (CVPR)

We present a method that gets as input an audio of violin or piano playing, and outputs a video of skeleton predictions which are further used to animate an avatar. The key idea is to create an animation of an avatar that moves their hands similarly to how a pianist or violinist would do, just from audio.

By: Eli Shlizerman, Lucio Dery, Hayden Schoen, Ira Kemelmacher Shlizerman

June 18, 2018

Detail-Preserving Pooling in Deep Networks

Computer Vision and Pattern Recognition (CVPR)

In this paper, we aim to leverage recent results on image downscaling for the purposes of deep learning.

By: Faraz Saeedan, Nicolas Weber, Michael Goesele, Stefan Roth

June 16, 2018

A common cause in the phenomenological and sensorimotor correlates of body ownership

International Multisensory Research Forum

The feeling that our limbs belong to our body is at the core of bodily self-consciousness. Over the years, limb ownership has been assessed through several types of measurements, including questionnaires and sensorimotor tasks assessing the perceived location of the hand with a visual-proprioceptive conflict.

By: Majed Samad, Cesare Parise, Sean Keller, Massimiliano Di Luca
Areas: AR/VR