Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it