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Conference Paper
The GENEA Challenge 2023: A large-scale evaluation of gesture generation models in monadic and dyadic settings
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- Authors
- Taras Kucherenko, Rajmund Nagy, Youngwoo Yoon, Jieyeon Woo, Teodor Nikolov, Mihail Tsakov, Gustav Eje Henter
- Issue Date
- 2023-10
- Citation
- International Conference on Multimodal Interaction (ICMI) 2023, pp.792-801
- Language
- English
- Type
- Conference Paper
- Abstract
- This paper reports on the GENEA Challenge 2023, in which participating teams built speech-driven gesture-generation systems using the same speech and motion dataset, followed by a joint evaluation. This year’s challenge provided data on both sides of a dyadic interaction, allowing teams to generate full-body motion for an agent given its speech (text and audio) and the speech and motion of the interlocutor. We evaluated 12 submissions and 2 baselines together with held-out motion-capture data in several large-scale user studies. The studies focused on three aspects: 1) the human-likeness of the motion, 2) the appropriateness of the motion for the agent’s own speech whilst controlling for the human-likeness of the motion, and 3) the appropriateness of the motion for the behaviour of the interlocutor in the interaction, using a setup that controls for both the human-likeness of the motion and the agent’s own speech. We found a large span in human-likeness between challenge submissions, with a few systems rated close to human mocap. Appropriateness seems far from being solved, with most submissions performing in a narrow range slightly above chance, far behind natural motion. The effect of the interlocutor is even more subtle, with submitted systems at best performing barely above chance. Interestingly, a dyadic system being highly appropriate for agent speech does not necessarily imply high appropriateness for the interlocutor. Additional material is available via the project website at svito-zar.github.io/GENEAchallenge2023/.
- KSP Keywords
- Body Motion, Dyadic interaction, Generation system, Gesture generation, Large span, Large-scale evaluation, Motion capture, User studies, natural motion