<p>In recent years, numerous studies on person re-identification (Re-ID) primarily emphasize visual appearance, leading to vulnerabilities like clothing changes, blurring, occlusions, and various other issues due to the reliance on visual cues. Earlier, promising methodologies encompass the integration of data from diverse modalities, such as gait silhouettes and infrared images, to alleviate the impact of imperfections in the visual data. Although gait is an important biometric features for human identification, effectively integrating them with RGB appearances remains challenging due to the substantial domain disparity. Rather than treating this as a new problem setting, we address the critical bottleneck of multimodal feature alignment. In this paper, we propose a novel and principled multimodal fusion strategy for person Re-ID. By introducing an efficient multimodal aware framework (MAF), our approach skillfully and automatically fuses RGB features with gait silhouette features without the need for explicit spatial calibration. To evaluate our approach, we introduce a new synthetic gait person Re-ID dataset, SGPR, involving RGB images and silhouettes. A comprehensive set of experiments has been conducted on multiple benchmark datasets and the results have clearly demonstrated the efficacy of our fusion strategy in this task.</p>