Extracting Main Modes of Human Shape Variation from 3-D Anthropometric Data
Bibliographic record
Abstract
Characterizing the variations of the human body shape is fundamentally important to many applications ranging from animation to product design. 3-D scanning technology makes it possible to digitize the complete surfaces of a large number of human bodies, providing much richer information about the body shape than the traditional anthropometric measurements. This technology opens up opportunities to extract new measurements for quantifying the body shape. Using the data from the first large scale 3-D anthropometric survey, the CAESAR project, we demonstrate that the human body shape can be represented by a small number of principal components. Principal Component Analysis extracts orthogonal basis vectors, called eigenpersons, from the space of body shapes. The shape of any individual person can then be expressed by the linear combination of the basis vectors. We demonstrate that some of these components correspond to the commonly used body measurements like height and weight and others indicate new ways of charactering body shape variations. We develop tools to visualize the changes of the body shape along the main components. These tools help understand the meaningful components of the human body shape.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".