Design and implementation of a deep learning based 3D face reconstruction from 2D images
Bibliographic record
Abstract
Nowadays, 3D reconstruction from images has played an important role in computer vision with many improvements in both quality and performance. One of its main uses is the generation of 3D models of objects that are difficult to model. This work mainly focuses on creating 3D models of human heads from 2D images using neural representations. However, these techniques have a significant limitation as their effectiveness is strictly dependent on the availability of a large number (several tens) of input views of the scene, and involve computationally expensive operations. In this work, the specific problem of full 3D head reconstruction is addressed by adapting coordinate-based representations with a probabilistic prior that allows for faster convergence and better generalization when using few input images. The reconstruction is done in 2 steps, first we learn a 3D model of the head shape from different point clouds using implicit representations. After that, the learned prior is used to initialize and constrain the geometry of the reconstruction. By doing so, we obtain high-fidelity reconstructions of the head, including the hair and shoulders, and with a high level of detail that exceeds state-of-the-art methods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".