Bibliographic record
Abstract
Artists frequently capture character poses via raster sketches, then use these drawings as a reference while posing a 3D character in a specialized 3D software --- a time-consuming process, requiring specialized 3D training and mental effort. We tackle this challenge by proposing the first system for automatically inferring a 3D character pose from a single bitmap sketch, producing poses consistent with viewer expectations. Algorithmically interpreting bitmap sketches is challenging, as they contain significantly distorted proportions and foreshortening. We address this by predicting three key elements of a drawing, necessary to disambiguate the drawn poses: 2D bone tangents, self-contacts, and bone foreshortening. These elements are then leveraged in an optimization inferring the 3D character pose consistent with the artist's intent. Our optimization balances cues derived from artistic literature and perception research to compensate for distorted character proportions. We demonstrate a gallery of results on sketches of numerous styles. We validate our method via numerical evaluations, user studies, and comparisons to manually posed characters and previous work. Code and data for our paper are available at http://www-labs.iro.umontreal.ca/bmpix/sketch2pose/.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.181 | 0.054 |
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".