A gaze-based study for investigating the perception of visual realism in simulated scenes
Why this work is in the frame
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Bibliographic record
Abstract
Visual realism has been a major objective of computer graphics since the inception of the field. However, the perception of visual realism is not a well-understood process and is usually attributed to a combination of visual cues and image features that are difficult to define or measure. For highly complex images, the problem is even more involved. The purpose of this paper is to present a study based on eye tracking for investigating the perception of visual realism of static images with different visual qualities. The eye-fixation clusters helped to define salient image features corresponding to 3D surface details and light transfer properties that attract observers' attention. This enabled the definition and categorization of image attributes affecting the perception of photorealism. The dynamics of the visual behavior of different observer groups were examined by analyzing saccadic eye movements. We also demonstrated how the different image categories used in the experiments were perceived with varying degrees of visual realism. The results presented can be used as a basis for investigating the impact of individual image features on the perception of visual realism. This study suggests that post-recall or simple abstraction of visual experience is not accurate and the use of eye tracking provides an effective way of determining relevant features that affect visual realism, thus allowing for improved rendering techniques that target these features.
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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 | 0.001 |
| 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 it