Ranking-Based Affect Estimation of Motion Capture Data in the Valence-Arousal Space
Why this work is in the frame
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Bibliographic record
Abstract
Affect estimation consists of building a predictive model of the perceived affect given stimuli. In this study, we are looking at the perceived affect in full-body motion capture data of various movements. There are two parts to this study. In the first part, we conduct groundtruthing on affective labels of motion capture sequences by hosting a survey on a crowdsourcing platform where participants from all over the world ranked the relative valence and arousal of one motion capture sequences to another. In the second part, we present our experiments with training a machine learning model for pairwise ranking of motion capture data using RankNet. Our analysis shows a reasonable strength in the inter-rater agreement between the participants. The evaluation of the RankNet demonstrates that it can learn to rank the motion capture data, with higher confidence in the arousal dimension compared to the valence dimension.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 it