360°全景照片，AI帮你找最佳取景角度
拍全景照片时，你转了一圈，但哪一帧最值得留？传统裁剪工具默认画面里已经有值得保留的构图，只问你裁哪里；而360°场景里，连往哪看都得先发现。PhotoGuider把这个问题拆成两步：先用四个「摄影令牌」——主体、构图、美感、完整性——给场景打分，再基于分数选出最佳视角并给出裁剪框，还能输出相机参数供原生重渲染。在盲测中，120人对100个全景场景打分，它选出的构图在53.2%的情况下排第一，远超两个强基线(27.3%和19.5%)。它不是你明天就能用的工具，但如果你拍过全景或VR内容，这就是「AI帮你找最佳取景」从想法变成可量化评测的第一步。
📄 原文摘要(英文)
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/