Sign language translation systems are complex and require many components. As
a result, it is very hard to compare methods across publications. We present an
open-source implementation of a text-to-gloss-to-pose-to-video pipeline
approach, demonstrating conversion from German to Swiss German Sign Language,
French to French Sign Language of Switzerland, and Italian to Italian Sign
Language of Switzerland. We propose three different components for the
text-to-gloss translation: a lemmatizer, a rule-based word reordering and
dropping component, and a neural machine translation system. Gloss-to-pose
conversion occurs using data from a lexicon for three different signed
languages, with skeletal poses extracted from videos. To generate a sentence,
the text-to-gloss system is first run, and the pose representations of the
resulting signs are stitched together.

本篇研究实现了一个从文字到手语的系统，并提出了三个不同的组件来实现文字到手语的转换，包括一个词形还原器、一个基于规则的单词重排和删除组件以及一个神经机器翻译系统。将手语姿势从视频中提取出来，用于三种不同的手语语言中的词汇表进行手语姿势转换。最后，将运行文字到手语系统，并将生成的手势姿势组合起来生成一句话。