PhotoGuider: Composition-Aware Cropping and 360^{circ} View Framing with Explainable Photographic Assessments
PhotoGuider: Composition-Aware Cropping and 360^{circ} View Framing with Explainable Photographic Assessments
PhotoGuider使用共享的构图感知token选择观看方向并框定360°全景图，同时输出带解释性评分的相机参数。该方法在两个标准2D裁剪基准上达到最高平均miou性能，在盲法排名研究中对100个全景场景的评估中，其框定结果在53.2%的情况下优于其他两种方法。
Published on Nov 2
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Abstract
PhotoGuider selects viewing directions and frames 360° panoramas using shared composition-aware tokens and produces camera-parameter outputs with explanation-conditioned scoring.
Traditional image cropping assumes the photograph already contains the composition worth keeping, and asks only where to crop. A full 360^{circ} 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. Our MLLM explainer is fine-tuned to generate composition-related explanations conditioned on images and measured token assessments. To our knowledge, PhotoGuider is the first method to combine explicit composition-based direction selection in 360^{circ} 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 cropping 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. Project page: https://ntuneillee.github.io/research/photoguider/
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