Quantitative Analysis of Visual Guidance in Level Transitions Using Multimodal Visual Metrics
Bibliographic record
Abstract
Visual guidance plays a crucial role in level design, while prior work has largely relied on qualitative observations. This study presents a novel quantitative framework for evaluating visual guidance during level transitions in 3D role-playing games. By integrating analyses of depth maps derived from raycast grids with high-resolution RGB image sequences from our primary dataset in Dark Souls III, we quantify metrics such as luminance dynamics, chromatic complexity, and spatial depth distribution. This bimodal analysis separates geometric factors (from depth) and perceptual factors (from color), thereby clarifying how specific visual cues consistent with design principles such as spatial funneling and chromatic contrast-influence player navigation and immersion. Our initial empirical findings, derived from strictly quantitative and numerical analyses, suggest that the synergy between geometric constraints and perceptual cues provides an effective framework for both validating design principles and identifying navigation pitfalls in level transitions. These results not only provide a formal, data-driven understanding of level design but also offer actionable insights for creating more intuitive virtual environments and establishing evaluation criteria for future procedural level generation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".