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Journal Article
Evaluating Gesture Generation in a Large-scale Open Challenge: The GENEA Challenge 2022
- Authors
- Taras Kucherenko, Pieter Wolfert, Youngwoo Yoon, Carla Viegas, Teodor Nikolov, Mihail Tsakov, Gustav Eje Henter
- Issue Date
- 2024-06
- Citation
- ACM Transactions on Graphics, v.43, no.3, pp.1-28
- ISSN
- 0730-0301
- Publisher
- Association for Computing Machinery (ACM)
- Language
- English
- Type
- Journal Article
- Abstract
- This article 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 articles, differences in results are here only due to differences between methods, enabling direct comparison between systems. The dataset was based on 18 hours of full-body motion capture, including fingers, of different persons engaging in a 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 has been a difficult problem in the field.
- KSP Keywords
- 18 hours, Benchmark data, Data-Driven, Generation system, Gesture generation, Motion capture, Research articles, Speech Signals, User study, body motion, direct comparison
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
