MDD: Masked Deconstructed Diffusion for 3D Human Motion Generation from Text
Bibliographic record
Abstract
We present MDD (Masked Deconstructed Diffusion), a novel framework for generating high-fidelity 3D human motions from textual descriptions. Our MDD framework employs a multi-stage Kinematic Chain Quantization (KCQ) that effectively encodes motion sequences into a compact yet expressive codebook by capturing both local and global human kinematic features. This codebook is then leveraged by a Masked Deconstructed Diffusion Transformer (MDDT), which takes text inputs and iteratively refines the output motion sequence through masked index prediction in a deconstructed diffusion process. By aligning the prediction with the denoising process, our method strikes an optimal balance between generation quality and computational efficiency. Extensive evaluations on multiple established benchmarks demonstrate that MDD consistently outperforms state-of-the-art methods in terms of precision and semantic accuracy, while achieving superior inference speed. Our generated motions are further validated in multiple virtual reality (VR) scenes, showcasing the effectiveness of our framework in VR applications.
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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) | 0.001 | 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".