Application and Optimization of 3D Model Building Technology in Film and Television Special Effects
Bibliographic record
Abstract
The combination of technology and art in film and television special effects can greatly enhance the visual impact of film and television animation, and improve its commercial and artistic value.The study elaborates on the generation of special effects in 3D modeling technology in film and television production, and based on the application of rigid body special effects, it proposes a highly efficient rigid body crushing mode for optimization in response to the problems such as low real-time performance in rigid body crushing simulation.The model is a particle-based real-time simulation method of object crushing under the impact of external forces, using the discrete unit method to represent the inter-particle force, and proposes an inverse crushing mechanism, which realizes the particle-based DEM simulation on the GPU.Experimental results show that the simulation method of rigid body crushing constructed in this paper can meet the simulation requirements in different scene scales, and the rendering rate in small-scale and large-scale scenes is 90~155FPS and 40~50FPS, respectively, which is not only realistic but also real-time, and can meet the requirements of film and television production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".