3D-Aware Semantic-Guided Generative Model for Human Synthesis
Bibliographic record
Abstract
Generative Neural Radiance Field (GNeRF) models, which extract<br> implicit 3D representations from 2D images, have recently been shown to produce<br> realistic images representing rigid/semi-rigid objects, such as human faces<br> or cars. However, they usually struggle to generate high-quality images representing<br> non-rigid objects, such as the human body, which is of a great interest for<br> many computer graphics applications. This paper proposes a 3D-aware Semantic-<br> Guided Generative Model (3D-SGAN) for human image synthesis, which combines<br> a GNeRF with a texture generator. The former learns an implicit 3D representation<br> of the human body and outputs a set of 2D semantic segmentation<br> masks. The latter transforms these semantic masks into a real image, adding a<br> realistic texture to the human appearance. Without requiring additional 3D information,<br> our model can learn 3D human representations with a photo-realistic,<br> controllable generation. Our experiments on the DeepFashion dataset show that<br> 3D-SGAN significantly outperforms the most recent baselines. The code is available<br> at https://github.com/zhangqianhui/3DSGAN.
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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 | 0.001 | 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 | 0.007 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".