Hierarchical retargetting of 2D motion fields to the animation of 3D plant models
Bibliographic record
Abstract
The complexity of animating trees, shrubs and foliage is an impediment to the efficient and realistic depiction of natural environments. This paper presents an algorithm to extract, from a single video sequence, motion fields of real shrubs under the influence of wind, and to transfer this motion to the animation of complex, synthetic 3D plant models. The extracted motion is retargeted without requiring physical simulation. First, feature tracking is applied to the video footage, allowing the 2D position and velocity of automatically identified features to be clustered. A key contribution of the method is that the hierarchy obtained through statistical clustering can be used to synthesize a 2D hierarchical geometric structure of branches that terminates according to the cut-off threshold of a classification algorithm. This step extracts both the shape and the motion of a hierarchy of features groups that are identified as geometrical branches. The 2D hierarchy is then extended to three dimensions using the estimated spatial distribution of the features within each group. Another key contribution is that this 3D hierarchical structure can be efficiently used as a motion controller to animate any complex 3D model of similar but non-identical plants using a standard skinning algorithm. Thus, a single video source of a moving shrub becomes an input device for a large class of virtual shrubs. We illustrate the results on two examples of shrubs and one outdoor tree. Extensions to other outdoor plants are discussed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.010 | 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".