The effects of depth warping on perceived acceleration in stereoscopic animation
Bibliographic record
Abstract
Stereoscopic media produce the sensation of depth through differences between the images presented to the two eyes. These differences arise from binocular parallax which in turn is caused by the separation of the cameras used to capture the scene. Creators of stereoscopic media face the challenge of depicting compelling depth while restricting the amount of parallax to a comfortable range. To address this tradeoff, stereoscopic warping or depth adjustment algorithms are used in the post-production process to selectively increase or decrease the depth in specific regions. This process modifies the image's depth-to-parallax mapping to suit the desired parallax range. As the depth is adjusted using non-linear parallax re-mapping functions, the geometric stereoscopic space is distorted. In addition, the relative expansion or compression of stereoscopic space should theoretically affect the perceived acceleration of an object passing through that region. Here we evaluate this prediction and determine if stereoscopic warping affects viewers' perception of acceleration. Observers judged the perceived acceleration of an approaching object (a toy helicopter) moving in depth through a complex stereoscopic 3D scene. The helicopter flew at one of two altitudes, either ground level or camera level. For each altitude, stereoscopic animations were produced under three depth re-mapping conditions (i) compressive, (ii) expansive, and (iii) zero (no re-mapping) for a total of six test conditions. We predicted that expansive depth re-mapping would produce a bias toward perceiving deceleration of the approaching helicopter, while compressive depth remapping would result in a bias toward seeing acceleration. However, there were no significant differences in the amount or direction of bias between the re-mapping conditions. We did find a significant effect of the helicopter altitude, such that there was little bias in acceleration judgements when the helicopter moved at ground level but a significant bias towards reporting acceleration when the helicopter moved at camera level. This result is consistent with the proposal that observers can make use of additional monocular (2D) cues in the ground level condition to improve their acceleration estimates. The lack of an effect of depth re-mapping suggests that viewers have considerable tolerance to depth distortions resulting from stereoscopic post-processing. These results have important implications for effective post-production and quality assurance for stereoscopic 3D content creation.
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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".