ShutterMuse: Capture-Time Photography Guidance with MLLMs
For photographers and AI researchers, this work addresses the underexplored area of capture-time guidance, providing a benchmark and model that outperforms existing methods in composition refinement and pose recommendation.
The paper introduces CaptureGuide-Bench, a benchmark for capture-time photography guidance covering both composition and subject pose, and develops ShutterMuse, a unified MLLM that achieves the best overall photographer-side performance among baselines and competitive subject-side pose recommendation with lower inference cost.
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.