presto: Efficient, Training-free, and Open-world Object Placement via Imaginary Search

2026-08-21

AI Summary

该研究通过多模态大语言模型引导的启发式搜索方法，解决开放场景中物体放置问题，提出无需训练的框架presto以实现高效、适应性强的对象布局。

Abstract

Object placement is critical in image composition, requiring spatially and semantically coherent positioning of objects within diverse scenes. Existing approaches typically rely on hand-crafted rules or supervised learning on limited datasets, which restricts their generalization and interpretability, especially in open-world scenarios involving novel objects and scenes. In this work, we reformulate open-world object placement as a heuristic search task guided by reasoning from a Multimodal Large Language Model (MLLM). We introduce \textsf{presto}, a zero-shot, training-free framework that operates within an imaginary action space to iteratively refine object position and scale. Our coarse-to-fine search strategy ensures fast convergence, and we evaluate two decision-making variants: Metric-guided Selection and MLLM-as-a-judge. Experiments across multiple benchmarks show that \textsf{presto}~achieves state-of-the-art performance, particularly in previously unseen, open-world settings. Human studies further reveal that the MLLM-as-a-judge variant produces more perceptually coherent placements than metric-driven approaches, highlighting a gap between standard evaluation metrics and human visual judgment.

Problem

• object placement
• open-world scenarios
• generalization
• interpretability

Innovation

• zero-shot
• training-free
• open-world object placement
• heuristic search
• Multimodal Large Language Model