A Practical Recipe for Structured Pruning of MotionGPT: Dependency-Graph Pruning and FFN Channel Reduction
Bibliographic record
Abstract
Human motion generation from natural language has become an important research challenge in computer vision and natural language processing, with applications in animation, robotics, and immersive VR/AR systems. Recent advances such as MotionGPT treat motion as a language sequence, enabling unified text-to-motion generation, captioning, and prediction across datasets like HumanML3D. However, the large size of such models limits their deployment in real-world settings. This paper presents a practical and reproducible pruning framework for MotionGPT, a state-of-the-art multimodal generative model that treats human motion as a foreign language. Two complementary structured pruning strategies are proposed: (1) dependency-graph pruning, a global, structure-aware method implemented with Torch-Pruning that removes channels consistently across residual connections, tied projections, and multi-head attention; and (2) FFN channel pruning, a local procedure tailored to T5-style DenseReluDense feed-forward blocks, which shrinks the dominant intermediate dimension. Experiments on HumanML3D show that the pruned model reduces parameters by ~14% while largely preserving semantic metrics such as Matching Score and R-Precision. Although FID increases ($0.22 \rightarrow 0.86$) and trajectory errors (ADE/FDE) worsen moderately, motion diversity remains high. Compared to prior models such as MDM, the pruned MotionGPT maintains competitive or superior alignment scores while being more efficient. These findings demonstrate that structured pruning provides a viable path to making large motion-language models lighter and more accessible.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".