PhotoGuider: Composition-Aware Cropping and 360° View Framing with Explainable Photographic Assessments

Traditional image cropping assumes the photograph already contains the composition worth keeping, and asks only where to crop. A full 360 scene offers no such guarantee: the viewing direction in a scene has to be discovered before anything can be framed. We present PhotoGuider, a measure-then-frame method that answers both questions with compact learned scorers over one shared set of composition-aware features. The designed features are extracted using foundation and task-specific vision models and structured into four photographic tokens: Subject, Composition, Aesthetics, and Integrity. Fine-tuned on and prompted with these measured tokens, our MLLM explainer provides composition-related explanations grounded in measurement rather than image content only. To our knowledge, PhotoGuider is the first method to combine explicit composition-based direction selection in 360 panoramas with refined framing, camera-parameter output for native re-rendering, and measurement-conditioned explanations. On two standard 2D cropping benchmarks, PhotoGuider achieves the highest average mIoU performance against ground-truth crops. In a blind ranking study, 120 participants ranked three methods over 100 panoramic scenes from the public Poly Haven HDRI library, placing our framings first in 53.2% of cases, against 27.3% and 19.5% for two strong alternatives.