ShotCrop$^3$: Cropping Human-Centric Images into Cinematic Triple-Shot Compositions
For creative professionals needing multi-shot compositions from single images, this work provides a novel task and method, though the improvement is measured against a general-purpose model (GPT-5) rather than specialized baselines.
The paper introduces Triple-Shot Compositions (TSC), a task to generate three cinematic crops (establishing, medium, close-up) from a single human-centric image, and proposes ShotCrop, a model trained via three-stage process including Chain-of-Thought fine-tuning, semi-supervised learning with pseudo-labels, and GRPO optimization. ShotCrop achieves 2.82x improvement over GPT-5 in shot localization accuracy on the TSC-Bench benchmark.
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.