Prior work on aesthetic composition typically produces a single aesthetically pleasing crop, overlooking the narrative value of composing multiple shots from one scene. In practice, multi-shot composition is critical for downstream creative workflows: commercial posters often require multiple crops with different emphases (e.g., context, subject, and emotion/product details) to present key story beats. Therefore, we propose \textbf{Triple-Shot Compositions (TSC)}, a composition task that generates a three-shot set -- establishing, medium, and close-up -- from a single human-centric image, each paired with a brief shot description to support visual narration. To learn TSC with limited expert annotations, we introduce \textbf{ShotCrop} which undergoes a three-stage training process: it first applies Chain-of-Thought supervised fine-tuning to establish basic reasoning and aesthetic shot-cropping skills, then performs semi-supervised fine-tuning with high-confidence pseudo labels to further enhance aesthetic capability, and is finally optimized with Group Relative Policy Optimization for \textbf{ShotCrop} (GRPO-S) using a composite reward tailored for it. Specifically, our pseudo-labeling strategy combines MLLM-based scoring, aesthetic assessment, and CLIP similarity to retain high-confidence training signals. In addition, we present TSC-Bench, a benchmark of 1.2k expert-annotated test cases. Notably, ShotCrop achieves an average improvement of \textbf{2.82} times over GPT-5 in shot localization accuracy.

翻译：此前关于美学构图的研究通常仅生成单个美观的裁剪结果，忽视了从同一场景中组合多个镜头所蕴含的叙事价值。实际上，多镜头构图对下游创意工作流至关重要：商业海报常需不同侧重点的多张裁剪（如场景、主体、情感/产品细节）以呈现关键剧情节点。为此，我们提出**三镜头构图任务（Triple-Shot Compositions, TSC）**，该任务要求从单张人物中心图像中生成包含定场镜头、中景镜头和特写镜头的三镜头组合，每张镜头附带简短描述以支持视觉叙事。为在专家标注有限的情况下学习TSC，我们引入**ShotCrop**，其训练流程包含三个阶段：首先通过思维链监督微调建立基础推理与美学裁剪能力，随后利用高置信度伪标签进行半监督微调以进一步增强美学性能，最终通过针对**ShotCrop**的群体相对策略优化（GRPO-S），配合为其定制的复合奖励函数完成优化。具体而言，我们的伪标签策略结合了基于多模态大模型的评分、美学评估与CLIP相似度，以保留高置信度训练信号。此外，我们提出包含1200个专家标注测试用例的基准数据集TSC-Bench。值得注意的是，ShotCrop在镜头定位准确度上相较于GPT-5实现了平均**2.82倍**的提升。