Creating impactful characters
Bibliographic record
Abstract
Human motion capture using video-based or sensor-based methods gives animators the capability to directly translate complex human motions to create lifelike character animations. Advances in motion capture algorithms have improved their accuracy for estimating human generalized motion coordinates (joint angles and body positions). However, the traditional motion capture pipeline is not well suited to measure short duration, high acceleration impacts, such as running and jumping footstrikes. While high acceleration impacts have minimal influence on generalized coordinates, they play a big role in exciting soft tissue dynamics. Here we present a method for correcting motion capture trajectories using a sparse set of inertial measurement units (IMUs) collecting at high sampling rates to produce more accurate impact accelerations without sacrificing accuracy of the generalized coordinates representing gross motions. We demonstrate the efficacy of our method by correcting human motion captured experimentally using commercial motion capture systems with high rate IMUs sampling at 400Hz during basketball jump shots and running. With our method, we automatically corrected 185 jumping impacts and 1266 running impacts from 5 subjects. Post correction, we found an average increase of 84.6% and 91.1% in pelvis vertical acceleration and ankle dorsiflexion velocity respectively for basketball jump shots, and an average increase of 110% and 237% in pelvis vertical acceleration and ankle plantarflexion velocity respectively for running. In both activities, pelvis vertical position and ankle angle had small corrections on average below 2.0cm and 0.20rad respectively. Finally, when driving a human rig with soft tissue dynamics using corrected motions, we found a 143.4% and 11.2% increase in soft tissue oscillation amplitudes in basketball jump shots and running respectively. Our methodology can be generalized to correct impact accelerations for other body segments, and provide new tools to create realistic soft tissue animations during dynamic activities for more lifelike characters and better motion reconstruction for biomechanical analyses.
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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".