  We present SignAvatars, the first large-scale, multi-prompt 3D sign language
(SL) motion dataset designed to bridge the communication gap for Deaf and
hard-of-hearing individuals. While there has been an exponentially growing
number of research regarding digital communication, the majority of existing
communication technologies primarily cater to spoken or written languages,
instead of SL, the essential communication method for Deaf and hard-of-hearing
communities. Existing SL datasets, dictionaries, and sign language production
(SLP) methods are typically limited to 2D as annotating 3D models and avatars
for SL is usually an entirely manual and labor-intensive process conducted by
SL experts, often resulting in unnatural avatars. In response to these
challenges, we compile and curate the SignAvatars dataset, which comprises
70,000 videos from 153 signers, totaling 8.34 million frames, covering both
isolated signs and continuous, co-articulated signs, with multiple prompts
including HamNoSys, spoken language, and words. To yield 3D holistic
annotations, including meshes and biomechanically-valid poses of body, hands,
and face, as well as 2D and 3D keypoints, we introduce an automated annotation
pipeline operating on our large corpus of SL videos. SignAvatars facilitates
various tasks such as 3D sign language recognition (SLR) and the novel 3D SL
production (SLP) from diverse inputs like text scripts, individual words, and
HamNoSys notation. Hence, to evaluate the potential of SignAvatars, we further
propose a unified benchmark of 3D SL holistic motion production. We believe
that this work is a significant step forward towards bringing the digital world
to the Deaf and hard-of-hearing communities as well as people interacting with
them.
