Gloss Semantic-Enhanced Network with Online Back-Translation for Sign Language Production
Shengeng Tang, Richang Hong, Dan Guo, Meng Wang
Abstract
Sign Language Production (SLP) aims to generate the visual appearance of sign language according to the spoken language, in which a key procedure is to translate sign Gloss to Pose (G2P). Existing G2P methods mainly focus on regression prediction of posture coordinates, namely closely fitting the ground truth. In this paper, we provide a new viewpoint: a Gloss semantic-Enhanced Network is proposed with Online Back-Translation (GEN-OBT) for G2P in the SLP task. Specifically, GEN-OBT consists of a gloss encoder, a pose decoder, and an online reverse gloss decoder. In the gloss encoder based on the transformer, we design a learnable gloss token without any prior knowledge of gloss, to explore the global contextual dependency of the entire gloss sequence. During sign pose generation, the gloss token is aggregated onto the existing generated poses as gloss guidance. Then, the aggregated features are interacted with the entire gloss embedding vectors to generate the next pose. Furthermore, we design a CTC-based reverse decoder to convert the generated poses backward into glosses, which guarantees the semantic consistency during the processes of gloss-to-pose and pose-to-gloss. Extensive experiments on the challenging PHOENIX14T benchmark demonstrate that the proposed GEN-OBT outperforms the state-of-the-art models. Visualization results further validate the interpretability of our method.