Estimating Virtual Camera FOV to Reduce Perspective Shape Distortion in 2D-to-3D Face Reconstruction
Bibliographic record
Abstract
Existing image-based 3D face reconstruction methods rely on a virtual camera to project the reconstructed 3D face onto the 2D image plane for comparison with the input image, a crucial step for accurate results. To simplify the reconstruction process, these methods often use fixed camera intrinsics and assume minimal perspective distortion, overlooking the varying distortion levels in "in-the-wild" images and leading to inaccuracies in reconstructed 3D face shapes. To address this issue, we propose estimating the virtual camera’s optimal field-of-view (FOV) for a given image, enabling consistent 3D face reconstruction across varying distortion levels. We introduce two synthetic datasets: one to train our FOV estimation network (FOV-Net) and another to evaluate its performance and reconstruction accuracy. We use the FOV-Net predicted FOV to initialize the camera, which is used in the fitting-based reconstruction process. Experiments show that our approach significantly improves reconstruction consistency under different levels of perspective distortion.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".