Towards Generating Digital LIBRAS Signers on Mobile Devices
Wellington Silveira, Andrew Alaniz, Marina Hurtado, Luca Mendonça, Rodrigo de · 2023
Sign language is an effective way to communicate with people who have some degree of hearing impairment. However, mastery of such languages is limited to a relatively small number of people. In this context, the use of assistive technologies is an excellent ally in social inclusion. Approaches based on graphics and deep learning have emerged as an effective non-intrusive way to perform sign language recognition (SLR), translation (SLT), and production (SLP). Pose transfer for sign language reenactment by digital signers is one of the tasks that can be carried out with these techniques. Despite the existence of methods addressing this problem in the literature, few of them tackle the deployment of such models on platforms accessible to real users. Therefore, in this paper, we propose the adaptation and implementation of a deep generative model for Brazilian Sign Language (LIBRAS) production on a mobile device. This effort aims toward the generation of synthetic digital LIBRAS signers which could be used to enhance real-time communication with hearing-impaired people.