Towards a GENEA Leaderboard -- an Extended, Living Benchmark for Evaluating and Advancing Conversational Motion Synthesis
<|reference_start|>Towards a GENEA Leaderboard -- an Extended, Living Benchmark for Evaluating and Advancing Conversational Motion Synthesis: Current evaluation practices in speech-driven gesture generation lack standardisation and focus on aspects that are easy to measure over aspects that actually matter. This leads to a situation where it is impossible to know what is the state of the art, or to know which method works better for which purpose when comparing two publications. In this position paper, we review and give details on issues with existing gesture-generation evaluation, and present a novel proposal for remedying them. Specifically, we announce an upcoming living leaderboard to benchmark progress in conversational motion synthesis. Unlike earlier gesture-generation challenges, the leaderboard will be updated with large-scale user studies of new gesture-generation systems multiple times per year, and systems on the leaderboard can be submitted to any publication venue that their authors prefer. By evolving the leaderboard evaluation data and tasks over time, the effort can keep driving progress towards the most important end goals identified by the community. We actively seek community involvement across the entire evaluation pipeline: from data and tasks for the evaluation, via tooling, to the systems evaluated. In other words, our proposal will not only make it easier for researchers to perform good evaluations, but their collective input and contributions will also help drive the future of gesture-generation research.<|reference_end|>
@article{nagy2024towards,
    title={Towards a GENEA Leaderboard -- an Extended, Living Benchmark for
  Evaluating and Advancing Conversational Motion Synthesis},
    author={Rajmund Nagy, Hendric Voss, Youngwoo Yoon, Taras Kucherenko, Teodor
  Nikolov, Thanh Hoang-Minh, Rachel McDonnell, Stefan Kopp, Michael Neff,
  Gustav Eje Henter},
    journal={arXiv preprint arXiv:2410.06327},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06327},
    primaryClass={cs.HC cs.CV cs.GR cs.LG}
}