Reconstructing Signing Avatars From Video Using Linguistic Priors

TL;DR

This paper introduces SGNify, a novel method that automatically reconstructs expressive 3D signing avatars from monocular videos using linguistic priors, improving accuracy and naturalness over previous approaches.

Contribution

SGNify is the first approach to fully automatically generate detailed 3D signing avatars from in-the-wild monocular videos using universal linguistic priors.

Findings

SGNify outperforms state-of-the-art methods in 3D hand and body pose estimation.

Reconstructed avatars are significantly more comprehensible and natural.

Perceptual studies confirm the quality of SGNify's 3D reconstructions.

Abstract

Sign language (SL) is the primary method of communication for the 70 million Deaf people around the world. Video dictionaries of isolated signs are a core SL learning tool. Replacing these with 3D avatars can aid learning and enable AR/VR applications, improving access to technology and online media. However, little work has attempted to estimate expressive 3D avatars from SL video; occlusion, noise, and motion blur make this task difficult. We address this by introducing novel linguistic priors that are universally applicable to SL and provide constraints on 3D hand pose that help resolve ambiguities within isolated signs. Our method, SGNify, captures fine-grained hand pose, facial expression, and body movement fully automatically from in-the-wild monocular SL videos. We evaluate SGNify quantitatively by using a commercial motion-capture system to compute 3D avatars synchronized with monocular video. SGNify outperforms state-of-the-art 3D body-pose- and shape-estimation methods on SL videos. A perceptual study shows that SGNify's 3D reconstructions are significantly more comprehensible and natural than those of previous methods and are on par with the source videos. Code and data are available at $\href{http://sgnify.is.tue.mpg.de}{\text{sgnify.is.tue.mpg.de}}$.