Weixuan Ding

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

This work reformulates open-world object placement as a heuristic search task guided by reasoning from a Multimodal Large Language Model (MLLM), and introduces Presto, a zero-shot, training-free framework that operates within an imaginary action space to iteratively refine object position and scale.