A Facial Motion Retargeting Pipeline for Appearance Agnostic 3D Characters
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
3D facial motion retargeting has the advantage of capturing and recreating the nuances of human facial motions and speeding up the time-consuming 3D facial animation process. However, the facial motion retargeting pipeline is limited in reflecting the facial motion's semantic information (i.e., meaning and intensity), especially when applied to nonhuman characters. The retargeting quality heavily relies on the target face rig, which requires time-consuming preparation such as 3D scanning of human faces and modeling of blendshapes. In this paper, we propose a facial motion retargeting pipeline aiming to provide fast and semantically accurate retargeting results for diverse characters. The new framework comprises a target face parameterization module based on face anatomy and a compatible source motion interpretation module. From the quantitative and qualitative evaluations, we found that the proposed retargeting pipeline can naturally recreate the expressions performed by a motion capture subject in equivalent meanings and intensities, such semantic accuracy extends to the faces of nonhuman characters without labor-demanding preparations.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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