Hierarchical Approaches for Generating Stylistic Human Motions from Audio
Bibliographic record
Abstract
Generating realistic and plausible human motions driven by audio has been explored for decades and has a wide range of applications in real life, such as virtual reality, gaming, films, and human-computer interaction. However, the spatiotemporal complexity of human motion and the requirement for generated movements to align with audio features pose significant challenges to motion synthesis algorithms. In this thesis, we first explore the approaches to human motion learning and synthesis, with a particular focus on data-driven dance and gesture generation. Then we propose a two-level framework to synthesize dance and gesture motions based on the music and speech input respectively, where the high-level motion planner models the overall structure of the motion sequence and the low-level implementer incorporates audio influences to generate detailed movements with nuances. Moreover, we introduce a motion editing module that refines generated dance. Building on this, we propose a method for mapping user inputs to dance style, giving users further control over the generated dance, as well as a data-driven approach for mapping speech audio to gesture style. Extensive experiments conducted on two datasets demonstrate the superior effectiveness and efficiency of our proposed method in generating diverse, high-quality dance and gesture motions that are well-synchronized with audio inputs.
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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 | 0.000 |
| 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) | 0.013 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".