Controllable Modular Trees
Bibliographic record
Abstract
Trees are a ubiquitous part of virtual environments, but faithfully modeling trees is challenging because of their incredible diversity. Tree modeling methods face two key challenges, as users want to create trees that look realistic but retain control over key elements of their appearance, such as the shape of the tree crowns. Traditional tree modeling methods are geared to produce tree models that are both detailed and unique and are typically stored as high-triangle count meshes or skeletons plus radii. Storing and manipulating such traditional tree models comes with a significant memory cost. Recent approaches have proposed to use modular trees consisting of a finite set of branch modules, which are transformed (scaled, translated, and rotated) to jointly form realistic looking trees. Consequently, modular trees require orders of magnitude less memory than their traditional counterparts. However, existing methods for modeling modular trees focus on visual realism and provide only minimal mechanisms for artists to control the look and shape of the resulting trees. We propose a controllable method for modeling modular trees, enabling artists to define the tree’s overall crown shape while maintaining a plausible appearance using a small set of replicated branch modules. We formulate the computation of realistic modular trees that conform to a user-specified crown shape as a constrained mixed-variable optimization problem. We then compute the trees that satisfy these constraints using a method that grows trees one layer of branches at a time, maintaining realism throughout and promoting accurate approximation of the target crown shape. We extensively test our method on diverse crown shapes and compare against baselines, demonstrating its effectiveness.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".