Text-based three-dimensional geometric person retrieval

Person Re-identification (Re-ID) is crucial in computer vision, widely applied in forensic investigation, intelligent surveillance, and video retrieval. Recent text-based Re-ID methods leverage eyewitness descriptions to enhance retrieval flexibility but still face challenges in accurately characterizing individuals under complex conditions. To address issues like low resolution, viewpoint variations, and occlusions, this paper proposes a novel text-based person Re-ID approach that integrates textual descriptions with synthesized Three-dimensional (3D) geometric pedestrian data derived from existing Two-dimensional (2D) images. Specifically, the semantic richness of text compensates for the lack of color and texture details in 3D data, while the robustness of geometric and pose information significantly enhances retrieval performance. Despite current 3D pedestrian data being generated through reconstruction algorithms, this work serves as a pioneering exploration of text-to-3D pedestrian retrieval, offering substantial potential for real-world applications in multimodal biometrics, forensic investigations, and privacy protection. Experiments on three public datasets demonstrate that our method achieves competitive performance, confirming its practical applicability and significance. • First text-based 3D person Re-ID using text and 3D geometry. • Novel framework uses text semantics and 3D geometry to handle occlusions. • Experiments on three datasets show competitive real-world performance.