Detection of inconsistency between shape and motion in realistic female and male animation
Bibliographic record
Abstract
Capturing the motion of a person and retargeting it to a virtual character with a different body shape is common practice in computer animation. This inconsistency between motion and shape often makes animations look unrealistic. It remains unclear which aspects of an animation affect how realistic it is perceived. Previous research has found detection of the inconsistency between motion and shape in biometric virtual characters to be at chance level for actions that involve object manipulation. Here, we test whether similar results are obtained for actions not involving objects and compare the detection of inconsistency in realistic female and male animation for actions with and without object manipulation. For creating our stimuli, we used the animations of five pairs of female and male performers with large differences in body weight from the bmlRUB database when throwing a ball, lifting a box, jumping, and walking. For each actor pair, we created inconsistent animations by combining the body shape from one actor with the motion from the other actor. For the consistent stimuli, the body shape and motion came from the same actor. In each trial of the experiment, participants observed one consistent and one inconsistent animation and selected which of the two they perceived to be inconsistent. Our results showed that for both female and male animations, participants’ detection rate was above chance for walking, and was at chance level for throwing. For lifting and jumping, the detection rate was at chance level for female animations and above chance level for male animations. Overall, detection rate was low which is promising news for realistic human animations but tended to be higher for male animations. Future research should investigate a broader range of actions to determine which are perceptually most affected by motion retargeting.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".