  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.
