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Dominant Person Search methods aim to localize and recognize query persons ina unified network, which jointly optimizes two sub-tasks, \ie, detection andRe-IDentification (ReID). Despite significant progress, two major challengesremain: 1) Detection-prior modules in previous methods are suboptimal for theReID task. 2) The collaboration between two sub-tasks is ignored. To alleviatethese issues, we present a novel Person Search framework based on the Diffusionmodel, PSDiff. PSDiff formulates the person search as a dual denoising processfrom noisy boxes and ReID embeddings to ground truths. Unlike existing methodsthat follow the Detection-to-ReID paradigm, our denoising paradigm eliminatesdetection-prior modules to avoid the local-optimum of the ReID task. Followingthe new paradigm, we further design a new Collaborative Denoising Layer (CDL)to optimize detection and ReID sub-tasks in an iterative and collaborative way,which makes two sub-tasks mutually beneficial. Extensive experiments on thestandard benchmarks show that PSDiff achieves state-of-the-art performance withfewer parameters and elastic computing overhead.

PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement