Publication

Driving-Signal Aware Full-Body Avatars

ACM SIGGRAPH


Abstract

We present a learning-based method for building driving-signal aware full-body avatars. Our model is a conditional variational autoencoder that can be animated with incomplete driving signals, such as human pose and facial keypoints, and produces a high-quality representation of human geometry and view-dependent appearance. The core intuition behind our method is that better drivability and generalization can be achieved by disentangling the driving signals and remaining generative factors, which are not available during animation. To this end, we explicitly account for information deficiency in the driving signal by introducing a latent space that exclusively captures the remaining information, thus enabling the imputation of the missing factors required during full-body animation, while remaining faithful to the driving signal. We also propose a learnable localized compression for the driving signal which promotes better generalization, and helps minimize the influence of global chance-correlations often found in real datasets. For a given driving signal, the resulting variational model produces a compact space of uncertainty for missing factors that allows for an imputation strategy best suited to a particular application. We demonstrate the efficacy of our approach on the challenging problem of full-body animation for virtual telepresence with driving signals acquired from minimal sensors placed in the environment and mounted on a VR-headset.

Related Publications

All Publications

SIGGRAPH - August 2, 2021

Fast Diffraction Pathfinding for Dynamic Sound Propagation

Carl Schissler, Gregor Mückl, Paul Calamia

CVPR - June 21, 2021

img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation

Vítor Albiero, Xingyu Chen, Xi Yin, Guan Pang, Tal Hassner

ISMAR - July 29, 2021

Instant Visual Odometry Initialization for Mobile AR

Alejo Concha, Michael Burri, Jesus Briales, Christian Forster, Luc Oth

ICSA - November 6, 2019

Auralization systems for simulation of augmented reality experiences in virtual environments

Peter Dodds, Sebastià V. Amengual Garí, W. Owen Brimijoin, Philip W. Robinson

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy