Compact and efficient generation of radiance transfer for dynamically articulated characters
Bibliographic record
Abstract
We present a data-driven technique for generating the precomputed radiance transfer vectors of an animated character as a function of its joint angles. We learn a linear model for generating real-time lighting effects on articulated characters while capturing soft self-shadows caused by dynamic distant lighting. Indirect illumination can also be reproduced using our framework. Previous data-driven techniques have either restricted the type of lighting response (generating only ambient occlusion), the type of animated sequences (response functions to external forces) or have complicated runtime algorithms and incur non-trivial memory costs. We provide insights into the dimensionality reduction of the pose and coefficient spaces. Our model can be fit quickly as a preprocess, is very compact (~1 MB) and runtime transfer vectors are generated using a simple algorithm in real-time (> 100 Hz using a CPU-only implementation.) We can reproduce lighting effects on hundreds of trained poses using less memory than required to store a single mesh's PRT coefficients. Moreover, our model extrapolates to produce smooth, believable lighting results on novel poses and our method can be easily integrated into existing interactive content pipelines.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".