collaborators

5 papers

cs.CV2026

DiffCrossGait: Trajectory-Level Alignment for 2D-3D Cross-Modal Gait Recognition via Latent Diffusion

Zhiyang Lu, Ming Cheng

Cross-modal 2D-3D gait recognition is impeded by inherent domain discrepancies between 2D silhouette and 3D LiDAR range-view representations. While prior methods align only final e…

cs.CV2026

Text-guided Feature Disentanglement for Cross-modal Gait Recognition

Zhiyang Lu, Ming Cheng

Gait recognition is a biometric technique that identifies individuals based on their walking patterns, offering advantages in long-range, non-intrusive scenarios. However, real-wor…

cs.CV2026

Walking Further: Semantic-aware Multimodal Gait Recognition Under Long-Range Conditions

Zhiyang Lu, Wen Jiang, Tianren Wu +4

Gait recognition is an emerging biometric technology that enables non-intrusive and hard-to-spoof human identification. However, most existing methods are confined to short-range,…

cs.CV2024

STARFlow: Spatial Temporal Feature Re-embedding with Attentive Learning for Real-world Scene Flow

Zhiyang Lu, Qinghan Chen, Ming Cheng

Scene flow prediction is a crucial underlying task in understanding dynamic scenes as it offers fundamental motion information. However, contemporary scene flow methods encounter t…

cs.CV2024

SSRFlow: Semantic-aware Fusion with Spatial Temporal Re-embedding for Real-world Scene Flow

Zhiyang Lu, Qinghan Chen, Zhimin Yuan +1

Scene flow, which provides the 3D motion field of the first frame from two consecutive point clouds, is vital for dynamic scene perception. However, contemporary scene flow methods…