Bibliographic record
Abstract
This Zenodo repository contains Cartesian 3D coordinates in npy format for all test-set motion submitted by teams participating in the GENEA Challenge 2022. The npy format is easily readable using the NumPy package in Python. Attribution: If you use this material, please cite our latest paper on the GENEA Challenge 2022. At the time of writing (2022-08-04) 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/ Conditions FNA and UNA in the data contain motion from the Talking With Hands 16.2M dataset at https://github.com/facebookresearch/TalkingWithHands32M/. These are licensed under a CC BY NC 4.0 international license. The remaining 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/
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.127 | 0.001 |
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; both teacher heads agree on what is shown here.
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".