Online MoCap Data Coding With Bit Allocation, Rate Control, and Motion-Adaptive Post-Processing
Bibliographic record
Abstract
With the advancements in methods for capturing 3D object motion, motion capture (MoCap) data are starting to be used beyond their traditional realm of animation and gaming in areas such as the arts, rehabilitation, automotive industry, remote interactions, and so on. As the amount of MoCap data increase, compression becomes crucial for further expansion and adoption of these technologies. In this paper, we extend our previous work on low-delay MoCap data compression by introducing two improvements. The first improvement is the bit allocation to long-term and short-term reference MoCap frames, which provides a 10–15% reduction in coded bit rate at the same quality. The second improvement is the post-processing in the form of motion-adaptive temporal low-pass filtering, which is able to provide another 9–13% savings in the bit rate. The experimental results also indicate that the proposed online MoCap codec is competitive with several state-of-the-art offline codecs. Overall, the proposed techniques integrate into a highly effective online MoCap codec that is suitable for low-delay applications, whose implementation is provided alongside this paper to aid further research in the field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.004 | 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".