Person Re-Identification (Person Re-ID) has seen significant advancements due to the use of deep learning and large amounts of annotated training data. However, adapting a model trained in a fully annotated source domain to an unannotated target domain remains a challenge. This is primarily because of non-overlapping labels, substantial variation between the source and target domains (between-domain variation), and significant differences between camera views within the same domain (within-domain variation). Previous studies have addressed this issue by employing multi-domain translation using StarGAN, which translates source domain images into the style of target camera domains, leading to notable performance improvements for Re-ID models. However, the effectiveness of addressing both within-domain and between-domain variations using multi-domain translation in the image translation process has not been fully explored. In this study, we investigated this impact by validating two experimental configurations: treating datasets as domains (dataset-as-domain) and treating camera views as domains (camera-as-domain). Furthermore, we propose Person Semantic Information-Integrated StarGAN (PSI StarGAN), an enhanced version of StarGAN that integrates person semantic information and significantly improves the quality and reliability of translated images. Our experimental results demonstrate that PSI StarGAN enhances the Re-ID performance, especially in scenarios with large within-domain camera-view variations. This work is the first to integrate person semantic information into StarGAN for Unsupervised Domain Adaptive Person Re-ID.