DeepNRSfPP: Learning-Based Real-Time Non-Rigid Structure-from-Perspective Projection
Bibliographic record
Abstract
This paper, propose DeepNRSfPP, a novel hybrid learning-based framework for real-time non-rigid 3D reconstruction from monocular videos under perspective projection. Traditional non-rigid structure-from-motion (NRSfM) techniques often rely on orthographic assumptions and are computationally intensive, making them unsuitable for real-world, real-time applications. Our approach builds on the strengths of NRSfPP by integrating deep neural networks with geometric constraints, enabling fast and accurate reconstruction of dynamic, deformable surfaces from single-view video. Specifically, DeepNRSfPP leverages a deep temporal encoder to regress coarse 3D shapes and a perspective-aware optimization layer to refine reconstructions using motion smoothness and projection consistency. We evaluate our model on several benchmark datasets and demonstrate significant improvements over both classical and recent learning-based baselines in terms of accuracy and speed. Our system achieves real-time performance (>30 FPS) while maintaining fine-grained reconstruction quality, making it suitable for applications in augmented reality, human motion capture, and robotic perception.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".