Gesture2Vec: Clustering Gestures using Representation Learning Methods for Co-speech Gesture Generation
Why this work is in the frame
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Bibliographic record
Abstract
Co-speech gestures are a principal component in conveying messages and enhancing interaction experiences between humans and critical ingredients in human-agent interaction, including virtual agents and robots. Existing machine learning approaches have yielded only marginal success in learning speech-to-motion at the frame level. Current methods generate repetitive gesture sequences that lack appropriateness with respect to the speech context. To tackle this challenge, we take inspiration from successes in natural language processing on context and long-term dependencies, and propose a new framework that views text-to-gesture as machine translation, where gestures are words in another (non-verbal) language. We propose a vector-quantized variational autoencoder structure as well as training techniques to learn a rigorous representation of gesture sequences. We then translate input text into a discrete sequence of associated gesture chunks in the learned gesture space. Ultimately, we use translated gesture tokens from the input text as an input to the autoencoder's decoder to produce gesture sequences. Subjective and objective evaluations confirm the success of our approach in terms of appropriateness, human-likeness, and diversity. We also introduce new objective metrics using the quantized gesture representation.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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