Gait Recognition in the Wild

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

9 shown of 9 papers with code (12 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• MimicGait: A Model Agnostic approach for Occluded Gait Recognition using Correlational Knowledge Distillation
  26 Jan 2025 1 repository listed
  Gait recognition is an important biometric technique over large distances.
• It Takes Two: Accurate Gait Recognition in the Wild via Cross-granularity Alignment
  16 Nov 2024 1 repository listed
  In particular, the GCM aims to enhance the quality of parsing features by leveraging global features from silhouettes, while the PCM aligns the dynamics of human parts between silhouette and parsing features using the…
• GLGait: A Global-Local Temporal Receptive Field Network for Gait Recognition in the Wild
  13 Aug 2024 1 repository listed
  Recently, some Convolution Neural Networks (ConvNets) based methods have been proposed to address the issue of gait recognition in the wild.
• Parsing is All You Need for Accurate Gait Recognition in the Wild
  31 Aug 2023 1 repository listed
  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.
• Hierarchical Spatio-Temporal Representation Learning for Gait Recognition
  19 Jul 2023 1 repository listed Syntology ran 3 of 5 samples · 2 unverified · 5 pointer-only (licence)
  While current methods focus on exploiting body part-based representations, they often neglect the hierarchical dependencies between local motion patterns.
• LidarGait: Benchmarking 3D Gait Recognition with Point Clouds
  19 Nov 2022 1 repository listed
  Video-based gait recognition has achieved impressive results in constrained scenarios.
• Gait Recognition in the Wild with Multi-hop Temporal Switch
  1 Sep 2022 1 repository listed
  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…
• Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline
  5 May 2022 1 repository listed
  To the best of our knowledge, this is the first large-scale dataset for gait recognition in the wild.
• Gait Recognition in the Wild with Dense 3D Representations and A Benchmark
  6 Apr 2022 1 repository listed
  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…

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.