A ragdoll-less approach to physical animations of characters in vehicles
Bibliographic record
Abstract
Recently the use of vehicles has increased in importance for many games. This is not only for open-world games, where the use of vehicles are a crucial element of world traversal, but also for scenario based games where the use of vehicles adds a more varied game-play experience. In many of these games, however, the characters inside the vehicles lack the animations to connect their motion to that of the vehicle. The use of a few poses or small number of animations makes in-vehicle characters too rigid and is particularly noticeable in open vehicles or those with excessive motion such as tractors, speedboats or motorbikes. This can break the connection the player has to the vehicle experience. To solve this problem, several games have used a method to control a ragdoll with physical parameters to follow the input poses [Fuller and Nilsson 2010] [Mach 2017]. However, this solution has several complications regarding controllability and stability when simulating a ragdoll and a vehicle at the same time. I would like to introduce a new approach using particle-based dynamics rather than using a ragdoll. We present two methods: a particle-based approach to physical movement (see Figure 1) and modifying goal positions to generate plausible target poses (see Figure 3).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.009 | 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".