让机器人理解摄影美学并自动找最佳拍摄角度
PhotoAgent: A Robotic Photographer with Spatial and Aesthetic Understanding
- 用大模型推理将抽象美感转化为可计算的几何约束
- 通过3D高斯泼溅构建虚拟世界，快速模拟优化视角
- 适合需要智能摄影决策的机器人或自动化创作场景
用于创意任务（如摄影）的具身智能体必须弥合高层语言指令与几何控制之间的语义鸿沟。我们提出PhotoAgent，该智能体通过整合大模型多模态推理与新型控制范式实现这一目标。PhotoAgent首先利用大模型驱动的思维链推理，将主观美学目标转化为可求解的几何约束，从而由分析求解器计算出高质量初始视角。该初始姿态随后在基于3D高斯泼溅（3DGS）构建的逼真内部世界模型中，通过视觉反馈进行迭代优化。这种‘心理模拟’替代了耗时且昂贵的物理试错过程，实现了快速收敛至更优的视觉结果。评估表明，PhotoAgent在空间推理能力上表现优异，并显著提升了最终图像质量。
原文摘要 · Abstract (English)
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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