Perceptual validity in animation of human motion
Bibliographic record
Abstract
Abstract The crucial concept of modeling and synthesis/control of human motion (including face and body) for animation has been widely studied and explored in the literature. In this regard, the audience's perception of generated or recorded animation scenes is of critical importance. In this paper, we explore and conceptualize the general notions that need to be taken into account for human motion to maintain perceptual accuracy. We propose a paradigm called Perceptual Validity composed of four major components, which are discussed in detail. The model is concerned with different aspects of the scene such as correct illustration of the stimuli, context, and local/global relations of various visual cues present in human motion. Satisfying all the proposed principles, based on the literature, seems compulsory and vital for synthesis of perceptually valid animation scenes of human motion. We investigate the relative significance of the different components of the paradigm using feedback from expert animators and conduct a case study on one of the components of the paradigm. For further evaluation and exploration, Disney's principles of animation are discussed and compared against our proposed paradigm. We argue that while there are significant parallels and overlaps, our model is only focused on and more inclusive towards human motion and can therefore provide a valuable set of guidelines for animators in the field of character animation. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".