Co-speech gestures, gestures that accompany speech, play an important role in human communication. Automatic co-speech gesture generation is thus a key enabling technology for embodied conversational agents (ECAs), since humans expect ECAs to be capable of multi-modal communication. Research into gesture generation is rapidly gravitating towards data-driven methods. Unfortunately, individual research efforts in the field are difficult to compare: there are no established benchmarks, and each study tends to use its own dataset, motion visualisation, and evaluation methodology. To address this situation, we launched the GENEA Challenge, a gesture-generation challenge wherein participating teams built automatic gesture-generation systems on a common dataset, and the resulting systems were evaluated in parallel in a large, crowdsourced user study using the same motion-rendering pipeline. Since differences in evaluation outcomes between systems now are solely attributable to differences between the motion-generation methods, this enables benchmarking recent approaches against one another in order to get a better impression of the state of the art in the field. This paper reports on the purpose, design, results, and implications of our challenge.

中文翻译：

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基于公共数据的手势生成系统的大型众包评估：GENEA Challenge 2020

语音中的同语音手势在人类交流中起着重要的作用。因此，自动共语音手势生成是用于具体化的对话代理（ECA）的关键启用技术，因为人们希望ECA能够进行多模式通信。对手势生成的研究正迅速吸引到数据驱动方法。不幸的是，该领域的个人研究成果很难进行比较：没有既定的基准，并且每个研究都倾向于使用自己的数据集，运动可视化和评估方法。为了解决这种情况，我们发起了GENEA挑战赛，这是一种手势生成挑战，其中，参与团队在共同的数据集上构建了自动手势生成系统，并在大型，使用相同的运动渲染管道进行众包用户研究。由于现在系统之间的评估结果差异仅可归因于运动生成方法之间的差异，因此这可以使最新方法相互进行基准测试，以便更好地了解本领域的最新技术水平。本文报告了我们的挑战的目的，设计，结果和含义。