  This paper reports on the second GENEA Challenge to benchmark data-driven
automatic co-speech gesture generation. Participating teams used the same
speech and motion dataset to build gesture-generation systems. Motion generated
by all these systems was rendered to video using a standardised visualisation
pipeline and evaluated in several large, crowdsourced user studies. Unlike when
comparing different research papers, differences in results are here only due
to differences between methods, enabling direct comparison between systems.
This year's dataset was based on 18 hours of full-body motion capture,
including fingers, of different persons engaging in dyadic conversation. Ten
teams participated in the challenge across two tiers: full-body and upper-body
gesticulation. For each tier we evaluated both the human-likeness of the
gesture motion and its appropriateness for the specific speech signal. Our
evaluations decouple human-likeness from gesture appropriateness, which
previously was a major challenge in the field.
  The evaluation results are a revolution, and a revelation. Some synthetic
conditions are rated as significantly more human-like than human motion
capture. To the best of our knowledge, this has never been shown before on a
high-fidelity avatar. On the other hand, all synthetic motion is found to be
vastly less appropriate for the speech than the original motion-capture
recordings. Additional material is available via the project website at
https://youngwoo-yoon.github.io/GENEAchallenge2022/
