Bibliographic record
Abstract
Humans show effortless dexterity while manipulating objects using their own hands. However, specifying the motion of a virtual character's hand or of a robotic manipulator remains a difficult task that requires animation expertise or extensive periods of offline motion capture. We present Hands On: a real-time, adaptive animation interface, driven by compliant contact and force information, for animating contact and precision manipulations of virtual objects. Using our interface, an animator controls an abstract grasper trajectory while the full hand pose is automatically shaped by proactive adaptation and compliant scene interactions. Haptic force feedback enables intuitive control by mapping interaction forces from the full animated hand back to the reduced animator feedback space, invoking the same human sensorimotor processes utilized in natural precision manipulations. We provide an approach for online, adaptive shaping of the animated manipulator based on prior interactions, resulting in more functional and appealing motions. The importance of haptic feedback for authoring virtual object manipulations is verified in a user study with nonexpert participants that examines contact force trajectories while using our interface. Comparing the quality of motions produced with and without force rendering, haptic feedback is shown to be critical for efficiently communicating contact forces and dynamic events to the user.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.436 | 0.240 |
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".