Gait Recognition in the Wild
4 papers with code • 1 benchmarks • 1 datasets
Gait Recognition in the Wild refers to methods under real-world senses, i.e., unconstrained environment.
Gait3D
Most implemented papers
Gait Recognition in the Wild with Dense 3D Representations and A Benchmark
Gait3D/Gait3D-Benchmark
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CVPR 2022
Based on Gait3D, we comprehensively compare our method with existing gait recognition approaches, which reflects the superior performance of our framework and the potential of 3D representations for gait recognition in the wild.
Gait Recognition in the Wild with Multi-hop Temporal Switch
Current methods that obtain state-of-the-art performance on in-the-lab benchmarks achieve much worse accuracy on the recently proposed in-the-wild datasets because these methods can hardly model the varied temporal dynamics of gait sequences in unconstrained scenes.
LidarGait: Benchmarking 3D Gait Recognition with Point Clouds
shiqiyu/opengait
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CVPR 2023
Video-based gait recognition has achieved impressive results in constrained scenarios.
Parsing is All You Need for Accurate Gait Recognition in the Wild
Furthermore, due to the lack of suitable datasets, we build the first parsing-based dataset for gait recognition in the wild, named Gait3D-Parsing, by extending the large-scale and challenging Gait3D dataset.
