Planar interpolation with extreme deformation, topology change and dynamics
Bibliographic record
Abstract
We present a mesh-based, interpolatory method for interactively creating artist-directed inbetweens from arbitrary sets of 2D drawing shapes without rigging. To enable artistic freedom of expression we remove prior restrictions on the range of possible changes between shapes; we support interpolation with extreme deformation and unrestricted topology change. To do this, we extend discrete variational interpolation by introducing a consistent multimesh structure over drawings, a Comesh Optimization algorithm that optimizes our multimesh for both intra- and inter-mesh quality, and a new shape-space energy that efficiently supports arbitrary changes and can prevent artwork overlap when desired. Our multimesh encodes specified correspondences that guide interpolation paths between shapes. With these correspondences, an efficient local-global minimization of our energy interpolates n-way between drawing shapes to create inbetweens. Our Comesh Optimization enables artifact-free minimization by building consistent meshes across drawings that improve both the quality of per-mesh energy discretization and inter-mesh mapping distortions, while guaranteeing a single, compatible triangulation. We implement our method in a test-bed interpolation system that allows interactive creation and editing of animations from sparse key drawings with arbitrary topology and shape change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".