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BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models
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BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models
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Large vision models (LVM) based gait recognition has achieved impressive performance. However, existing LVM-based approaches may overemphasize gait priors while neglecting the intrinsic value of LVM itself, particularly the rich, distinct representations across its multi-layers. To adequately unlock LVM's potential, this work investigates the impact of layer-wise representations on downstream recognition tasks. Our analysis reveals that LVM's intermediate layers offer complementary properties across tasks, integrating them yields an impressive improvement even without rich well-designed gait priors. Building on this insight, we propose a simple and universal baseline for LVM-based gait recognition, termed BiggerGait. Comprehensive evaluations on CCPG, CAISA-B*, SUSTech1K, and CCGR\_MINI validate the superiority of BiggerGait across both within- and cross-domain tasks, establishing it as a simple yet practical baseline for gait representation learning. All the models and code will be publicly available.
Forward citations
Cited by 5 Pith papers
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MMGait: Towards Multi-Modal Gait Recognition
MMGait provides a new multi-sensor gait dataset and OmniGait baseline to support single-modal, cross-modal, and unified multi-modal person identification from walking patterns.
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EventGait: Towards Robust Gait Recognition with Event Streams
EventGait is a dual-stream spiking and cross-modal framework for event-based gait recognition that matches or exceeds RGB methods in normal conditions and significantly outperforms them in low light, supported by new ...
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GaitProtector: Impersonation-Driven Gait De-Identification via Training-Free Diffusion Latent Optimization
GaitProtector optimizes diffusion model latents to impersonate target identities in gait sequences, dropping Rank-1 identification accuracy from 89.6% to 15.0% on CASIA-B while keeping scoliosis diagnostic accuracy at 74.2%.
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BarbieGait: An Identity-Consistent Synthetic Human Dataset with Versatile Cloth-Changing for Gait Recognition
BarbieGait is a new synthetic gait dataset with identity-consistent cloth changes paired with the GaitCLIF model that improves cross-clothing recognition on the new data and existing benchmarks.
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DiffCrossGait: Trajectory-Level Alignment for 2D-3D Cross-Modal Gait Recognition via Latent Diffusion
DiffCrossGait reformulates 2D-3D gait recognition as trajectory-level alignment in an identity-relevant latent diffusion space using a Tri-Phase Alignment Strategy and achieves state-of-the-art results on SUSTech1K an...
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