Bibliographic record
Abstract
This Zenodo repository contains objective evaluation results for all test-set motion submitted by teams participating in the GENEA Challenge 2022. We caution the user that objective metrics are known to have poor correlation with actual, perceived motion quality. For definitions and explanations of the "Average jerk magnitude", "Average acceleration magnitude", and "Average Hellinger distance", please see the paper "Moving fast and slow: Analysis of representations and post-processing in speech-driven automatic gesture generation" by Kucherenko et al., published in the International Journal of Human–Computer Interaction in 2021. For a definition and explanation of the "Canonical correlation analysis (CCA) coefficient", see the paper "Speech-driven animation with meaningful behaviors" by Sadoughi and Busso, published in Speech Communication in 2019. Code for computing these objective metrics is available through the challenge webpage. Attribution: If you use this material, please cite our latest paper on the GENEA Challenge 2022. At the time of writing (2022-08-10) this is our ACM ICMI 2022 paper: Youngwoo Yoon, Pieter Wolfert, Taras Kucherenko, Carla Viegas, Teodor Nikolov, Mihail Tsakov, and Gustav Eje Henter. 2022. The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generation. In Proceedings of the ACM International Conference on Multimodal Interaction (ICMI '22). ACM. You can find the latest information and a BibTeX file on the project website: https://youngwoo-yoon.github.io/GENEAchallenge2022/ The material is available under a CC BY 4.0 international license, with the text provided in LICENSE.txt. To find more GENEA Challenge 2022 material on the web, please see: * https://youngwoo-yoon.github.io/GENEAchallenge2022/ * https://genea-workshop.github.io/2022/challenge/ If you have any questions or comments, please contact: * The GENEA Challenge & Workshop organisers
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.057 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".