The field of gait recognition and person reidentification is experiencing significant developments, with a focus on improving robustness to varying surveillance angles, clothing variations, and non-cooperative scenarios. Researchers are exploring innovative approaches to handle cross-vertical view scenarios, distorted silhouettes, and open-set conditions. One notable trend is the integration of multiple biometric cues, such as face, body shape, and gait, to enhance recognition accuracy in unconstrained environments. Another area of research is the creation of large-scale datasets that simulate real-world conditions, including extreme data variability factors, to stress-test existing methods and promote progress in the field. Noteworthy papers include: CVVNet, a frequency aggregation architecture that achieves state-of-the-art performance in cross-vertical-view gait recognition. Database-Agnostic Gait Enrollment using SetTransformers, a transformer-based framework for open-set gait enrollment that is dataset-agnostic and recognition-architecture-agnostic. Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait, a unified end-to-end system that integrates complementary biometric cues across face, gait, and body shape modalities. DetReIDX, a large-scale aerial-ground person dataset that provides a stress test for ReID under real-world conditions.