Benchmarking Drag⋆ for eye direction transformation and beyond
Bibliographic record
Abstract
Abstract Eye direction plays a crucial role in determining the quality of photographs containing human faces. Images where subjects look in inconsistent directions are often perceived as low-quality and discarded. While state-of-the-art deep generative models such as DragGAN, DragDiffusion, and DragonDiffusion (collectively referred to as Drag⋆) offer potential solutions for eye direction transformation, their effectiveness for this specific task remains unexplored. In this work, we systematically investigate the capability of Drag ⋆ for eye direction transformation. Our initial experiments reveal that these models in their original form cannot effectively perform this task. To address this limitation, we construct a specialized dataset (i.e., eye multi-direction dataset (EMDD)) and establish a comprehensive benchmark for evaluating methods through fine-tuning on our curated data. Our analysis demonstrates that fine-tuned models achieve satisfactory results when the angular difference between the directions of the source and target eyes is small. However, we observe significant performance degradation when large directional changes are necessary. Through detailed investigation, we uncover the underlying causes of these limitations and provide insights into the models’ failure modes. To overcome these challenges, we propose the edge-localized point selector and zero-latent source region replacement , which can alleviate the identified limitations. Experimental results demonstrate that our approach achieves substantial performance improvements for eye direction transformation, particularly in scenarios involving large angular changes.
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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 | 0.000 |
| 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".