Deformable Elliptical Particles for Predictive Mesh-Adaptive Crowds
Bibliographic record
Abstract
Crowd simulation is an essential tool in the modern animation toolkit with a wide variety of applications, ranging from urban planning and evacuation analysis to creating believable crowds in both film and games. Modelling the movements and behaviours exhibited in crowds of characters often relies heavily upon the fundamental representation of agents used in the simulation. We propose a mesh-adaptive deformable representation of agents to increase simulation fidelity, generate novel behaviour, and support diversity. Most existing methods use static primitive geometries to represent agents, which neglects the variety in character meshes and animation states. We present an efficient method for generating elliptical particles, which can deform to any mesh and animation state in real-time. The method is straightforward, robust, and exceptionally generalizable. We develop a novel steering methodology for our agent representation method that solves the subsequent challenges of incorporating dynamic asymmetric particle representations. The physically-based algorithm features predictive collision avoidance, incorporating an activation function that encodes the rotational uncertainty of agents. Our model exhibits realistic packing behaviour of agents under high-density conditions, as well as unpacking when the flow is unconstrained. In addition to the compelling qualitative behaviour generated by our model, we present statistical comparisons with existing methods. Our method intrinsically supports the steering of crowds of diverse mesh morphologies without ad-hoc character-specific rules, while affording artist-defined steering components in the character mesh.
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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) | 0.000 | 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".