Time Reversal and Simulation Merging for Target-Driven Fluid Animation
Bibliographic record
Abstract
We present an approach to control the animation of liquids. The user influences the simulation by providing a target surface which will be matched by a portion of the liquid at a specific frame of the animation; our approach is also effective for multiple target surfaces forming an animated sequence. A source simulation provides the context liquid animation with which we integrate the controlled target elements. From each target frame, we compute a target simulation in two parts, one forward and one backward, which are then joined together. The particles for the two simulations are initially placed on the target shape, with velocities sampled from the source simulation. The backward particles use velocities in the opposite direction as the forward simulation, so that the two halves join seamlessly. When there are multiple target frames, each target frame simulation is computed independently, and the particles from these multiple target simulations are later combined. In turn, the target simulation is joined to the source simulation. Appropriate steps are taken to select which particles to keep when joining the forward, backward, and source simulations. This results in an approach where only a small fraction of the computation time is devoted to the target simulation, allowing faster computation times as well as good turnaround times when designing the full animation. Source and target simulations are computed using an off-the-shelf Lagrangian simulator, making it easy to integrate our approach with many existing animation pipelines. We present test scenarios demonstrating the effectiveness of the approach in achieving a well-formed target shape, while still depicting a convincing liquid look and feel.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".