ShutterMuse：基於多模態大語言模型的拍攝時攝影指導

摘要

真實世界攝影需要在拍攝時對相機取景與被攝主體姿勢提供引導。然而，現有的美學裁剪基準主要評估事後裁剪預測，並忽略了被攝主體側的建議，使多模態大語言模型在拍攝時引導能力尚未被充分探索。為填補此缺口，我們提出CaptureGuide-Bench，此基準包含兩項互補任務：攝影師側的構圖決策與優化，以及被攝主體側的場景條件姿勢推薦。我們的評估揭示了限制：通用型多模態大語言模型能做出構圖決策，但缺乏精確的優化定位；而專門的美學裁剪模型雖能有效定位裁剪區域，卻僅限於優化功能；兩者皆無法提供可執行的姿勢引導。為支援模型開發，我們進一步建構CaptureGuide-Dataset，包含13萬筆樣本與文字理由及結構化視覺標註，並開發ShutterMuse，這是一個經由監督學習與強化學習微調的統一多模態大語言模型。在CaptureGuide-Bench上的實驗顯示，ShutterMuse在既有基準線中取得了最佳的整體攝影師側表現，而在被攝主體側姿勢推薦方面，則以顯著較低的推理成本達到了競爭力，展現多模態大語言模型作為影像拍攝過程中的互動式輔助工具的潛力。

English

Real-world photography requires capture-time guidance for both camera framing and subject pose. Yet existing aesthetic cropping benchmarks mainly evaluate post-hoc crop prediction and overlook subject-side recommendations, leaving the capture-time guidance capabilities of multimodal large language models (MLLMs) underexplored. To address this gap, we introduce CaptureGuide-Bench, a benchmark with two complementary tasks: photographer-side composition decision and refinement, and subject-side scene-conditioned pose recommendation. Our evaluation reveals limitations: general-purpose MLLMs can make composition decisions but lack precise refinement localization, while specialized aesthetic cropping models localize crops effectively but are limited to refinement; neither provides actionable pose guidance. To support model development, we further construct CaptureGuide-Dataset, comprising 130K samples with textual rationales and structured visual annotations, and develop ShutterMuse, a unified MLLM trained with supervised and reinforcement fine-tuning. Experiments on CaptureGuide-Bench show that ShutterMuse achieves the best overall photographer-side performance among evaluated baselines and competitive subject-side pose recommendation with substantially lower inference cost, demonstrating the potential of MLLMs as interactive assistants for photography during image capture.