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 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".