Robust Human Animation Skeleton Extraction Using Compatibility and Correctness Constraints
Bibliographic record
Abstract
The ability to automatically animate arbitrary 3D characters based on motion capture (MoCap) data has many applications in simulation, entertainment and multimedia transmission. However, defining trajectory key-points in human figures for animation without any manual intervention remains a challenging problem that makes complete automation difficult. To animate an articulated 3D character an animation skeleton needs to be extracted from, or be embedded into, a 3D model for deformation during animation. In conventional animation software, this process is mostly done manually by expert animators, which makes it a very tedious and time consuming step. The automatic rigging approaches proposed in the literature require a front facing model with neutral T-pose to accurately extract or embed an animation skeleton. We propose a fully automatic skeleton extraction approach based on optimization of constraints on human shape that can generate the animation skeleton, regardless of the model's orientation and position. Experimental results demonstrate the effectiveness of our approach. Robust skeleton extraction followed by efficient MoCap data compression can greatly improve the fidelity of 3D animated model transmission.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".