💡 The paper introduces PhotoFlow, a Director-Reviewer-Reflector agent that enables language-conditioned virtual photography in arbitrary 3D scenes. The problem addressed is to create an agent that can enter a 3D scene, infer a suitable shot based on scene information and language intent, and render a photograph without preselected camera pose or reference image. This task requires complex 3D spatial understanding and abstract aesthetic judgment, which are difficult to evaluate together.

The method proposed is a closed-loop camera search using the Director-Reviewer-Reflector agent. The Director builds a photographic blueprint and proposes candidate cameras, the Reviewer checks and critiques the proposals, and the Reflector converts failures into region memory and adjusts the search. The authors also introduce VPhotoBench, a benchmark of 47 open-license 3D scenes and 141 language-conditioned photography missions.

The results show that PhotoFlow achieves the strongest external quality-alignment composite and success rate among various methods, including one-shot prediction, single-chain reflection, anchor-bank selection, and random search, under a six-round rendering budget. The paper demonstrates that a language model-centered spatial agent can produce strong photographs in a setting that challenges both 3D reasoning and aesthetic choice, making language-conditioned virtual photography in arbitrary 3D scenes an executable agent task.