Human Head Stiffness Rendering
Bibliographic record
Abstract
Human head stiffness rendering is important in haptic interactive applications, because it defines a realistic physical interaction between human operator(s) and a human avatar created in a virtual environment. In this paper, we propose a hybrid method for rendering the appropriate stiffness property on a human head polygon mesh that combines popular haptic rendering approaches: study the sophisticated deformation behavior of a deformable object and then interpret and render this behavior as the resulting stiffness property on the individual's head mesh. The stiffness property is estimated from a registered and shape-adapted skull template mesh as a reference and modeled from the deformation behavior of soft tissue in a finite-element method (FEM) framework. Our method consists of different procedures, including facial landmark detection, model registration using the iterative closest point technique, adaptive shape modification processed with a modified weighted free-form deformation, and FEM simulation. After the stiffness property is rendered on a head polygon mesh, we perform a user study by inviting participants to experience the haptic feedback rendered from our results. According to the participants' feedback, the stiffness property of the head polygon mesh is properly rendered, because it satisfies their expectations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".