Back to Basics: Motion Representation Matters for Human Motion Generation Using Diffusion Model
Bibliographic record
Abstract
Diffusion models have emerged as a widely utilized and successful methodology in human motion synthesis. Task-oriented diffusion models have significantly advanced action-to-motion, text-to-motion, and audio-to-motion applications. In this paper, we investigate fundamental questions regarding motion representations and loss functions in a controlled study, and we enumerate the impacts of various decisions in the workflow of the generative motion diffusion model. To answer these questions, we conduct empirical studies based on a proxy motion diffusion model (MDM). We apply v loss as the prediction objective on MDM (vMDM), where v is the weighted sum of motion data and noise. We aim to enhance the understanding of latent data distributions and provide a foundation for improving the state of conditional motion diffusion models. First, we evaluate the six common motion representations in the literature and compare their performance in terms of quality and diversity metrics. Second, we compare the training time under various configurations to shed light on how to speed up the training process of motion diffusion models. Finally, we also conduct evaluation analysis on a large motion dataset. The results of our experiments indicate clear performance differences across motion representations in diverse datasets. Our results also demonstrate the impacts of distinct configurations on model training and suggest the importance and effectiveness of these decisions on the outcomes of motion diffusion models.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".