PhotoMaster: Learning Holistic Capture-Time Decisions for Guided Photography Reshooting

Capturing compelling photographs requires sequential capture-time decisions. However, existing guidance methods operate at the image level rather than modeling the decision process that diagnoses photographic dimensions (e.g., camera position, lighting) and resolves them into executable reshooting actions, offering limited actionable support for novices. We present PhotoMaster, a decision-driven multimodal framework for capture-time photography guidance. To model the decision-making process, we propose the Photography Decision Taxonomy (PDT) that defines photographic dimensions for diagnosing novice-captured photos and maps them to executable actions. To make this intuitive, PhotoMaster pairs these executable actions with a rendered visual target that previews the expected outcome. To learn this decision space, we introduce a two-fold data engine: Forward Decision Transfer distills professional photographers' decisions from masterworks to casual shots, while Decision Inversion applies inverse capture decisions via camera-controllable world models, collectively yielding over 230K PDT-annotated triplets. Evaluations demonstrate PhotoMaster's capability to produce expert-aligned diagnoses and highly preferred visual references, while a real-world user study confirms that novices achieve significantly better captures using our framework.