Realistic Depth Image Synthesis for 3D Hand Pose Estimation
Bibliographic record
Abstract
The training of depth image-based hand pose estimation model typically relies on real-life datasets which are expected to be 1) largescale and cover a diverse range of hand poses and hand shapes, and 2) always come with high-precision annotations. However, existing datasets in reality are rather limited in the above regards due to multitude practical constraints, with time and cost being the major concerns. This observation motivates us to propose an alternative approach, where hand pose model is primarily trained with synthesized hand depth images that closely mimicking the characteristic noise patterns of a specific depth camera make under consideration. It is achieved by firstly mapping a Gaussian distributed variable to certain specific non-i.i.d. (independent and identically distributed) depth noise pattern, and then transforming a “vanilla” noise-free synthetic depth image to a realistic-looking image. Extensive empirical experiments demonstrate that our approach is capable of generating camera-specific realistic-looking hand depth images with precise annotations; comparing to entirely relying on annotated real images, a hand pose model with better performance is obtained by using only a small fraction (10%) of annotated real images as well as our synthesized images.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".