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arXiv · 2603.22796

PhotoAgent: A Robotic Photographer with Spatial and Aesthetic Understanding

Lirong Che·Zhenfeng Gan·Yanbo Chen·Junbo Tan·Xueqian Wang
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Abstract

Embodied agents for creative tasks like photography must bridge the semantic gap between high-level language commands and geometric control. We introduce PhotoAgent, an agent that achieves this by integrating Large Multimodal Models (LMMs) reasoning with a novel control paradigm. PhotoAgent first translates subjective aesthetic goals into solvable geometric constraints via LMM-driven, chain-of-thought (CoT) reasoning, allowing an analytical solver to compute a high-quality initial viewpoint. This initial pose is then iteratively refined through visual reflection within a photorealistic internal world model built with 3D Gaussian Splatting (3DGS). This ``mental simulation'' replaces costly and slow physical trial-and-error, enabling rapid convergence to aesthetically superior results. Evaluations confirm that PhotoAgent excels in spatial reasoning and achieves superior final image quality.

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Lirong Che, Zhenfeng Gan, Yanbo Chen, Junbo Tan, Xueqian Wang. 2026-03-24. PhotoAgent: A Robotic Photographer with Spatial and Aesthetic Understanding. https://arxiv.org/abs/2603.22796

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