Physical simulation for probabilistic motion tracking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human motion tracking is an important problem in computer vision. Most prior approaches have concentrated on efficient inference algorithms and prior motion models; however, few can explicitly account for physical plausibility of recovered motion. The primary purpose of this work is to enforce physical plausibility in the tracking of a single articulated human subject. Towards this end, we propose a full-body 3D physical simulation-based prior that explicitly incorporates motion control and dynamics into the Bayesian filtering framework. We consider the humanpsilas motion to be generated by a ldquocontrol looprdquo. In this control loop, Newtonian physics approximates the rigid-body motion dynamics of the human and the environment through the application and integration of forces. Collisions generate interaction forces to prevent physically impossible hypotheses. This allows us to properly model human motion dynamics, ground contact and environment interactions. For efficient inference in the resulting high-dimensional state space, we introduce exemplar-based control strategy to reduce the effective search space. As a result we are able to recover the physically-plausible kinematic and dynamic state of the body from monocular and multi-view imagery. We show, both quantitatively and qualitatively, that our approach performs favorably with respect to standard Bayesian filtering methods.
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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) | 0.000 | 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 it