The Zero-shot Composed Person Retrieval (ZS-CPR) task aims to jointly utilize visual and textual information for retrieving specific individuals. Traditional person retrieval methods, whether image-based or text-based, often struggle to effectively leverage both types of information, resulting in reduced accuracy. To address this, the Composed Person Retrieval (CPR) task is introduced, which combines image and text information for target person retrieval. However, supervised CPR methods require costly manual annotation datasets that are currently unavailable. To overcome this, the ZS-CPR task is proposed, which leverages existing domain-related data to solve the CPR problem without relying on expensive annotations.