Compression and direct manipulation of complex blendshape models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present a method to compress complex blendshape models and thereby enable interactive, hardware-accelerated animation of these models. Facial blendshape models in production are typically large in terms of both the resolution of the model and the number of target shapes. They are represented by a single huge blendshape matrix, whose size presents a storage burden and prevents real-time processing. To address this problem, we present a new matrix compression scheme based on a hierarchically semi-separable (HSS) representation with matrix block reordering. The compressed data are also suitable for parallel processing. An efficient GPU implementation provides very fast feedback of the resulting animation. Compared with the original data, our technique leads to a huge improvement in both storage and processing efficiency without incurring any visual artifacts. As an application, we introduce an extended version of the direct manipulation method to control a large number of facial blendshapes efficiently and intuitively.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it