TL;DR
This paper proposes a novel and principled multimodal fusion strategy for person Re-ID that skillfully and automatically fuses RGB features with gait silhouette features without the need for explicit spatial calibration and introduces a new synthetic gait person Re-ID dataset, SGPR.
In practical gait recognition scenarios where only silhouette sequences are available without original videos, directly applying appearance-driven BigGait frameworks often leads to a mismatch between methodology and data conditions. To leverage the prior advantages of frozen Large Vision Models (LVMs) for universal fea...
A multi-modal Re-ID framework that combines appearance, facial features, and gait to accurately perform this task is presented, providing a strong defense against appearance-based identity errors.
Existing video person re-identification (ReID) methods still face three major challenges: limited spatial perception from RGB-only inputs, equal-weight local feature extraction under occlusion, and ineffective fusion of heterogeneous motion and appearance features. To address these issues, we propose an RGB-D Triple Py...
RestoreGait is introduced, an end-to-end framework designed to actively recover occluded gait cues through a lightweight pseudo-3D spatiotemporal decoupled inpainting module that decouples spatial contour restoration from temporal motion aggregation, thereby effectively utilizing visible information across multiple fra...
CDGaitFusion is introduced, a multimodal gait recognition framework that integrates shared motion patterns with individual-specific features that provides a reliable and extensible framework for mitigating gait feature degradation under complex real-world conditions.
A unified empirical evaluation on the Market-1501, Occluded-DukeMTMC, and MSMT17 datasets is presented, revealing that latent feature-space refinement and semantic cross-modal alignment offer superior stability, scalability, and robustness compared to explicit pixel-level generation.
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