Bibliographic record
Abstract
<JATS1:p>Want to be the next Andy Serkis as Gollum in Lord of the Rings? Or Zoe Saldana in Avatar? How about Seth MacFarlane in Ted? Or do you want to star in video games such as Fortnite, Call of Duty or Halo?</JATS1:p> <JATS1:p>If so, this book will tell you everything you need to know about acting for motion capture.</JATS1:p> <JATS1:p>This is the first book to provide an invaluable resource for the education of the next generation of performers in this exciting medium. Over the last 10 years, a revolution has occurred in digital production – video games have overtaken the film and TV industries in terms of production and revenues. Many video games derive their digital animation from human performance by means of motion and performance capture. Actors such as Andy Serkis and Troy Baker have won critical acclaim for their digital performance in games and film.</JATS1:p> <JATS1:p>The book includes contributions from practitioners working across the globe, including: actor Kezia Burrows; software developer Stéphane Dalbera; director Kate Saxon; a group of Japanese games directors; Jeremy Meunier, Head of Motion Capture at Moov studios, Montreal; Marc Morisseau, motion editor for Avatar; and a Chinese Motion Capture suit manufacturer.</JATS1:p>
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.453 | 0.337 |
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".