Bibliographic record
Abstract
When animating characters, a key goal is the creation of a believable, expressive performance that gives a character a unique personality with a distinct style of movement. While animators are skilled at creating expressive, personalized performances, it remains challenging to change performance-related aspects of movement in existing motion data. In recent years, motion data reuse has become increasingly important as recorded motion capture data has come into widespread use. This thesis investigates the use of a sparse set of pose-centric editing controls for editing existing motion data using techniques similar to those used by keyframe animators when they create new motion. To do this, this thesis proposes the use of motion extrema—the poses a character passes through when there is a significant change in movement—as a means for choosing effective pose-centric editing controls. First, I present algorithms for identifying motion extrema. Motion extrema can be associated with individual joints or the full body of the character. I introduce a set of approaches for identifying motion extrema; these include the use of extrema of differential measures and the explicit search for times at which the body or a joint is in a spatially extreme configuration. I then present three motion editing applications that use motion extrema as a foundation for applying motion edits. The first application, pose-centric editing, allows users to interactively change poses in a motion, and the system modifies the motion to respect existing ground contact. The second application—staggered poses, introduces a model of character pose that explicitly encodes how timing varies among motion extrema on different parts of the body. This timing variation is commonly used by animators to model overlapping action. By introducing an algorithm for finding timing variation on motion extrema in existing motion, this system enables users to make high-level changes to timing patterns to change overlap effects in existing motion. Finally, I present a procedural motion editing application that targets a specific aspect of motion style; this technique is called spatial exaggeration. Spatial exaggeration changes the geometric relationships among extreme poses. Such edits cause movement to appear more or less energetic. Overall, these applications demonstrate that performance-related aspects of existing motion can be edited using a sparse set of controls in the form of motion extrema.
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How this classification was reachedexpand
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) | 0.002 | 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 teacher head, 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".