The field of person re-identification is moving towards leveraging synthetic data and semantic cues to improve performance. Researchers are exploring new methods to generate high-quality synthetic data and to disentangle identity features from non-biometric features. The use of textual descriptions and attribute-based text knowledge is becoming increasingly popular to enhance the accuracy of person re-identification models. Noteworthy papers include: An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval, which proposes a pipeline for generating synthetic data and explores its effectiveness in diverse scenarios. DIFFER: Disentangling Identity Features via Semantic Cues for Clothes-Changing Person Re-ID, which introduces a novel adversarial learning method that leverages textual descriptions to disentangle identity features. LATex: Leveraging Attribute-based Text Knowledge for Aerial-Ground Person Re-Identification, which proposes a framework that adopts prompt-tuning strategies to leverage attribute-based text knowledge for aerial-ground person re-identification.