The goal of automatic Sign Language Production (SLP) is to translate spoken
language to a continuous stream of sign language video at a level comparable to
a human translator. If this was achievable, then it would revolutionise Deaf
hearing communications. Previous work on predominantly isolated SLP has shown
the need for architectures that are better suited to the continuous domain of
full sign sequences.
In this paper, we propose Progressive Transformers, a novel architecture that
can translate from discrete spoken language sentences to continuous 3D skeleton
pose outputs representing sign language. We present two model configurations,
an end-to-end network that produces sign direct from text and a stacked network
that utilises a gloss intermediary.
Our transformer network architecture introduces a counter that enables
continuous sequence generation at training and inference. We also provide
several data augmentation processes to overcome the problem of drift and
improve the performance of SLP models. We propose a back translation evaluation
mechanism for SLP, presenting benchmark quantitative results on the challenging
RWTH-PHOENIX-Weather-2014T(PHOENIX14T) dataset and setting baselines for future
research.

该论文提出了一种名为 Progressive Transformers 的新型架构，旨在将离散文本语言句子翻译成表示手语的连续 3D 骨架姿势输出，从而实现自动手语翻译，作者在文中还提出了几种数据增强处理方式以克服漂移问题并提高手语翻译性能，在 RWTH-PHOENIX-Weather-2014T 数据集上取得了令人瞩目的基础性成果，为未来的研究提供了基线。