Traditional photography composition approaches are dominated by 2D cropping-based methods. However, these methods fall short when scenes contain poorly arranged subjects. Professional photographers often employ perspective adjustment as a form of 3D recomposition, modifying the projected 2D relationships between subjects while maintaining their actual spatial positions to achieve better compositional balance. Inspired by this artistic practice, we propose photography perspective composition (PPC), extending beyond traditional cropping-based methods. However, implementing the PPC faces significant challenges: the scarcity of perspective transformation datasets and undefined assessment criteria for perspective quality. To address these challenges, we present three key contributions: (1) An automated framework for building PPC datasets through expert photographs. (2) A video generation approach that demonstrates the transformation process from less favorable to aesthetically enhanced perspectives. (3) A perspective quality assessment (PQA) model constructed based on human performance. Our approach is concise and requires no additional prompt instructions or camera trajectories, helping and guiding ordinary users to enhance their composition skills.

翻译：传统摄影构图方法主要基于二维裁剪技术。然而，当场景中存在布局欠佳的主体时，这些方法往往效果有限。专业摄影师常采用视角调整作为三维重构手段，通过改变主体在投影平面上的二维关系（同时保持其实际空间位置）来实现更优的构图平衡。受此艺术实践的启发，我们提出摄影视角构图方法，其超越了传统的裁剪式构图框架。然而，PPC的实施面临两大挑战：视角变换数据集的稀缺性，以及视角质量评估标准的缺失。为解决这些难题，我们提出三项核心贡献：（1）通过专业摄影作品构建PPC数据集的自动化框架。（2）展示从欠佳视角向美学增强视角转换过程的视频生成方法。（3）基于人类审美表现构建的视角质量评估模型。本方法简洁高效，无需额外提示指令或相机轨迹，能有效帮助和引导普通用户提升构图技能。