Pith. sign in

REVIEW 2 major objections 4 minor 43 references

DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition

T0 review · 2 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read DepthGait proposes RGB-derived depth maps as a new gait-recognition modality and reports top accuracy on three benchmarks.

desk verdict Novel use of monocular depth for gait recognition, but the paper never isolates whether depth maps add geometric information beyond what an RGB or silhouette-derived control would provide; the CASIA-B state-of-the-art claim rests on a depth-only variant. read the letter →

arxiv 2508.03397 v1 pith:KG6ABHHT submitted 2025-08-05 cs.CV cs.MM

Xinzhu Li , Juepeng Zheng , Yikun Chen , Xudong Mao , Guanghui Yue , Wei Zhou , Chenlei Lv , Ruomei Wang
show 2 more authors
Fan Zhou Baoquan Zhao
This is my paper
classification cs.CVcs.MM
keywords GaitrecognitionDepthmapsMonocularestimationSilhouettesequencesMultimodalfusionMulti-scaleattentionCross-level
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that depth maps estimated from ordinary RGB video carry 3D body-geometry cues, such as stride, limb placement, and torso shape, that binary silhouettes and skeletons miss, and that adding them as a second input modality improves gait recognition under clothing, bag, and viewpoint changes. To test this, the authors build DepthGait, a two-branch network that feeds silhouettes and depth maps through a multi-stage encoder and fuses them at every stage with multi-scale attention and cross-level feature summation. On three benchmarks the fused model reports mean rank-1 accuracy of 87.6% on CCPG, 87.6% on SUSTech1K, and 91.2% on CASIA-B, and a depth-only variant already outperforms silhouette- and skeleton-based peers on CCPG and CASIA-B. The practical point is that existing RGB surveillance footage, without LiDAR or depth sensors, could be retrofitted with a depth-estimation step to strengthen biometric identification.

What carries the argument

The carrying mechanism is the multi-scale cross-level fusion (MCF) module applied at every encoder stage. Given stage features $F_s$ and $F_d$ from the silhouette and depth branches, it concatenates them, runs parallel 1x1-3x3-1x1 and 1x1-5x5-1x1 convolution chains to form local and global scores, and a softmax over the two modality channels produces per-location weights $W_S$; the fused map is $X_f = F_s W_S[:,0] + F_d W_S[:,1]$, and the stage output is $Y_f = X_f + F_{sd}$, where $F_{sd}$ is the encoder's stage output, so fused features feed the next stage. This stage-wise fusion lets low-level geometry from the depth map and appearance from the silhouette reinforce each other progressively, which the ablations show is more effective than single-stage or late fusion.

What would settle it

Replace the depth map in the pipeline with a silhouette-derived surrogate, such as the distance transform or shaded copy of the silhouette, and retrain under the same fusion scheme; if accuracy matches DepthGait, the depth estimator adds no geometric signal beyond the silhouette, and the paper's central claim fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that depth maps estimated from ordinary RGB frames make gait recognition more accurate than the two dominant input modalities, silhouettes and skeletons, because they encode per-pixel distance to the camera and preserve 3D body geometry that appearance-based masks discard. The preprocessing step converts estimated depth $d$ to normalized disparity $q=1/d$, crops it with the silhouette's bounding box, aligns it on the silhouette's vertical center, and resizes both to 64 by 44 pixels so the two modalities share geometry. DepthGait then runs the two sequences through separate branches of the same multi-stage encoder and, at every stage, fuses the feature maps with multi-scale attention and adds the fused result back into both branches. On the three tested benchmarks the depth-only variant already beats all compared silhouette and skeleton methods on CCPG and CASIA-B, and the fused DepthGait reports the highest mean rank-1 accuracy on all three: 87.6% on CCPG, 87.6% on SUSTech1K, and 91.2% on CASIA-B.

Load-bearing premise

The load-bearing premise is that a monocular depth-estimation model, applied to small 64-by-44 crops and masked by the silhouette, recovers genuine 3D body-shape information that is not already contained in the silhouette itself.

Editorial extensions

If this is right

  • Because depth maps are derived from the same RGB frames that already feed silhouette pipelines, existing surveillance datasets can be re-processed with depth estimation and re-evaluated without any new sensor hardware.
  • The depth-only variant's margins over silhouette and skeleton methods on CCPG and CASIA-B imply that adding a depth branch is a direct accuracy lever wherever RGB video is available.
  • The ablation results indicate that fusing at every encoding stage, rather than once at a late stage, is what preserves modality-specific detail; late single-stage fusion loses several accuracy points.
  • The reported person re-identification gains on CCPG (95.2% mean rank-1) suggest the depth-plus-silhouette representation generalizes beyond gait to identity matching from video.
  • On SUSTech1K the method improves mean rank-1 over the best peer in each category by 6.3%, with the largest gains in normal, bag, and occlusion conditions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Extension not tested in the paper: swapping the monocular depth estimator for a stereo or LiDAR depth source would separate the method's geometric hypothesis from the artifacts of any single estimator; the paper's formulation predicts the gains would persist.
  • The authors note their own night-condition weakness on SUSTech1K (38.4% rank-1, below two peers), which implies the depth branch inherits silhouette-segmentation failures; a testable fix is jointly refining segmentation and depth rather than masking precomputed silhouettes.
  • The paper evaluates only the benchmark viewing angles, so a further test of the view-invariance claim would train on a subset of viewpoints and probe unseen angles; if depth geometry is truly view-consistent, the drop should be smaller for depth than for silhouette.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. The paper introduces DepthGait, a dual-branch gait recognition network that combines binary silhouette sequences with depth maps estimated from RGB frames using the Depth Anything foundation model. The depth maps are converted to disparity, normalized per frame, masked by the silhouette, and aligned to the silhouette grid; both branches use DeepGaitV2-style encoders, and a multi-scale cross-level fusion (MCF) module with multi-scale attention is applied at every encoder stage. The method is evaluated on CCPG, SUSTech1K, and CASIA-B*, reporting mean rank-1 accuracies of 87.6%, 87.6%, and 91.2% respectively, and the CCPG ablations show monotonic accuracy gains when the depth branch, MSF, and CLF are added.

Significance. If the depth-induced gains are genuine, the work offers a practical way to inject 3D body-geometry cues into silhouette-based gait recognition using existing monocular RGB infrastructure, without LiDAR or multi-view capture. The paper is clearly structured, compares against a broad set of gait modalities and fusion baselines, and its CCPG ablation is clean enough to show that each proposed component helps. The depth-only variant (DepthGait★) also outperforms several established silhouette and skeleton methods, which is encouraging. The main unverified premise is that RGB-derived depth maps contribute geometric information beyond what is already contained in the silhouette and the RGB pixels; the current experiments do not isolate that factor.

major comments (2)
  1. [§3.2, §4.4 (Tables 2 and 5)] The central claim that RGB-derived depth maps provide 3D cues not available in silhouettes is not supported by the current experiments. The depth maps are generated from the same RGB frames, then masked with the silhouette, per-frame min-max normalized in disparity space, and resized to the silhouette grid; after this pipeline the depth input is highly correlated with the silhouette's shape and interior intensity profile. The paper never compares against a control branch that receives the same RGB pixels (e.g., cropped/aligned grayscale RGB), a silhouette distance transform, or a second copy of the silhouette with the same network capacity and fusion. Therefore the improvements in Tables 2 and 5 (e.g., 83.3 to 87.6 in Table 5) could be due to extra capacity, a second pathway, or low-level intensity information rather than depth-specific geometry. The SUSTech1K night result in Table 3 (38.4%, below ParsingGait and SkeletonGait++) is consistent with the depth maps inheriting silhouette failures, as the authors acknowledge. I request a control experiment that removes the depth-specific signal while preserving pathway and capacity.
  2. [§4.3, Table 4] The CASIA-B evaluation is presented as supporting state-of-the-art performance, but Table 4 reports only the depth-only variant ('Depth Map / DepthGait ours') and does not include the full fused DepthGait or DeepGaitV2 in the comparison. The abstract and conclusion refer to the overall method, so Table 4 cannot substantiate that claim. Please report the fused model on CASIA-B and include the missing recent baselines, or explicitly scope the claim to the depth-only variant.
minor comments (4)
  1. [§3.2] The normalization description is internally inconsistent: the text sets the silhouette height to 64 pixels and also says the standardized width w is set to 64, while the implementation later reports an input size of 64×44. Please clarify the exact crop/resize dimensions and whether height or width is fixed.
  2. [§4.4] The final sentence of Section 4.4 promises a visualization analysis comparing depth maps and contour maps, but no such visualization or analysis appears in the paper.
  3. [Table 5] The rows are labeled only with checkmarks under 'Baseline', 'Depth Map', 'MSF', and 'CLF', and the caption does not define whether 'Baseline' denotes the silhouette-only input or the DeepGaitV2 framework. This makes row 2 easy to misread as still containing a silhouette branch; please clarify the notation in the caption.
  4. [§4.4, Table 5] The incremental gains attributed to MSF and CLF in the text do not match the table: row 2 to row 3 is +2.3/+2.1 (exclude/include), not +3.5/+3.1, and row 3 to row 4 is +0.8/+0.5, not +4.3/+3.6. The cited numbers appear to be cumulative gains over the silhouette baseline rather than incremental gains over the preceding row, so the wording 'further improved' should be corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the method is trained and evaluated on standard external benchmarks; depth maps come from an external pretrained estimator, and the fusion network is a new architecture rather than a restatement of its inputs.

full rationale

The paper's derivation chain is empirical, not algebraic: RGB frames are converted to silhouettes (dataset-provided) and depth maps via the external Depth Anything model [36], then aligned, encoded by the DeepGaitV2 backbone [8], fused by the proposed MCF module, and trained with triplet and cross-entropy losses on official train/test splits. The state-of-the-art claim is supported by benchmark tables against published methods and by ablations. No equation defines a predicted quantity in terms of fitted parameters that are then presented as a prediction; no test-set values are fitted and renamed as predictions; no uniqueness theorem or load-bearing premise is justified solely by the authors' own prior work. The single self-citation ([20], MSPoint-Gait) appears only in a related-work enumeration and does not ground any central claim. The skeptic's concern that depth maps derived from the same RGB frames and masked by silhouettes may largely re-encode silhouette shape is a legitimate limitation or control issue, but it is an empirical confound rather than a circular derivation: the paper's central results are independently falsifiable on external benchmarks and do not reduce to their inputs by construction.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The work rests on the reliability of a pretrained monocular depth estimator and on dataset-supplied silhouettes, plus standard supervised training assumptions. The pseudo-depth representation is not a physically measured modality, so its information content is bounded by the RGB input.

free parameters (2)
  • loss weights alpha and beta = not reported
    The combined loss in Eq. (7) uses alpha and beta to balance triplet and cross-entropy losses; the paper does not give their values.
  • depth input width and height = 64 x 44 for CCPG; unspecified for other datasets
    Input size is chosen by hand and affects the feature maps; no ablation on this size is provided.
assumptions (3)
  • domain assumption Depth Anything provides useful monocular depth estimates for small, cropped, low-resolution gait silhouettes.
    Invoked in Section 3.2 when depth maps are generated and preprocessed; no depth-quality evaluation is reported.
  • domain assumption The silhouette masks used to crop and align depth maps are accurate enough.
    The paper crops depth maps using silhouette-derived boundaries (Figure 4) and admits in Section 4.3 that poor night silhouettes degrade depth maps.
  • domain assumption DeepGaitV2 is a suitable feature extractor for both modalities when used with the proposed fusion.
    The encoder is adopted from [8] without re-validation on depth inputs; the paper relies on its generality.

how reviews work

0 comments
Cite this review

Pith. "Pith review of DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition." pith.science (2026). https://pith.science/paper/KG6ABHHT

@misc{pith2026250803397,
  author       = {Pith},
  title        = {Pith review of: DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KG6ABHHT}},
  note         = {Machine review of arXiv:2508.03397}
}
read the original abstract

Robust gait recognition requires highly discriminative representations, which are closely tied to input modalities. While binary silhouettes and skeletons have dominated recent literature, these 2D representations fall short of capturing sufficient cues that can be exploited to handle viewpoint variations, and capture finer and meaningful details of gait. In this paper, we introduce a novel framework, termed DepthGait, that incorporates RGB-derived depth maps and silhouettes for enhanced gait recognition. Specifically, apart from the 2D silhouette representation of the human body, the proposed pipeline explicitly estimates depth maps from a given RGB image sequence and uses them as a new modality to capture discriminative features inherent in human locomotion. In addition, a novel multi-scale and cross-level fusion scheme has also been developed to bridge the modality gap between depth maps and silhouettes. Extensive experiments on standard benchmarks demonstrate that the proposed DepthGait achieves state-of-the-art performance compared to peer methods and attains an impressive mean rank-1 accuracy on the challenging datasets.

Figures

Figures reproduced from arXiv: 2508.03397 by the authors.

Figure 1
Figure 1. Quantitative evaluation on the SUSTech1K dataset [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The architecture of the proposed DepthGait model for robust gait recognition. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 4
Figure 4. Normalization of silhouettes and depth maps. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: The detailed structure of MSSE. where M-ATF(·) represents Softmax(·). To enhance the represen￾tation ability of features, we use a cross-level fusion method to obtain the final gait representation 𝑌𝑓 at this stage, while obtaining higher-level semantic information and …
Figure 6
Figure 6. Figure 6: The performance of DepthGait in different at [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

43 extracted references · 40 canonical work pages

  1. [1]

    Gunawan Ariyanto and Mark S Nixon. 2011. Model-based 3D gait biometrics. In 2011 international joint conference on biometrics (IJCB) . IEEE, 1–7

    work page 2011

  2. [2]

    Imed Bouchrika and Mark S Nixon. 2007. Model-based feature extraction for gait analysis and recognition. In Computer Vision/Computer Graphics Collaboration Techniques: Third International Conference, MIRAGE 2007, Rocquencourt, France, March 28-30, 2007. Proceedings 3 . Springer, 150–160

    work page 2007

  3. [3]

    Francisco M Castro, Manuel J Marin-Jimenez, Nicolás Guil, and Nicolás Pérez de la Blanca. 2020. Multimodal feature fusion for CNN-based gait recognition: an empirical comparison. Neural Computing and Applications 32 (2020), 14173– 14193

    work page 2020

  4. [4]

    Hanqing Chao, Yiwei He, Junping Zhang, and Jianfeng Feng. 2019. Gaitset: Regarding gait as a set for cross-view gait recognition. InProceedings of the AAAI conference on artificial intelligence , Vol. 33. 8126–8133

    work page 2019

  5. [5]

    Adrian Cosma and Emilian Radoi. 2024. PsyMo: A Dataset for Estimating Self- Reported Psychological Traits From Gait. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (W ACV). 4603–4613

    work page 2024

  6. [6]

    Yufeng Cui and Yimei Kang. 2023. Multi-Modal Gait Recognition via Effective Spatial-Temporal Feature Fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . 17949–17957

    work page 2023

  7. [7]

    Ruben Delgado-Escano, Francisco M Castro, Julian Ramos Cozar, Manuel J Marin- Jimenez, and Nicolas Guil. 2018. An end-to-end multi-task and fusion CNN for inertial-based gait recognition. IEEE Access 7 (2018), 1897–1908

    work page 2018

  8. [8]

    Chao Fan, Saihui Hou, Yongzhen Huang, and Shiqi Yu. 2024. Exploring Deep Models for Practical Gait Recognition. arXiv:2303.03301 [cs.CV] https://arxiv. org/abs/2303.03301

    arXiv 2024

Show all 43 references
  1. [9]

    Chao Fan, Junhao Liang, Chuanfu Shen, Saihui Hou, Yongzhen Huang, and Shiqi Yu. 2023. OpenGait: Revisiting Gait Recognition Towards Better Practicality. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 9707–9716

    2023

  2. [10]

    Chao Fan, Jingzhe Ma, Dongyang Jin, Chuanfu Shen, and Shiqi Yu. 2024. Skele- tonGait: Gait Recognition Using Skeleton Maps. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 38. 1662–1669

    2024

  3. [11]

    Chao Fan, Yunjie Peng, Chunshui Cao, Xu Liu, Saihui Hou, Jiannan Chi, Yongzhen Huang, Qing Li, and Zhiqiang He. 2020. Gaitpart: Temporal part-based model for gait recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 14225–14233

    2020

  4. [12]

    Passos, Rafael Gonçalves Pires, Daniel Felipe Silva Santos, Lucas Pascotti Valem, Thierry P

    Claudio Filipi Gonçalves dos Santos, Diego de Souza Oliveira, Leandro A. Passos, Rafael Gonçalves Pires, Daniel Felipe Silva Santos, Lucas Pascotti Valem, Thierry P. Moreira, Marcos Cleison S. Santana, Mateus Roder, Jo Paulo Papa, et al. 2022. Gait recognition based on deep le...

    2022

  5. [13]

    Yang Fu, Yunchao Wei, Yuqian Zhou, Honghui Shi, Gao Huang, Xinchao Wang, Zhiqiang Yao, and Thomas Huang. 2019. Horizontal pyramid matching for person re-identification. In Proceedings of the AAAI conference on artificial intelligence , Vol. 33. 8295–8302

    2019

  6. [14]

    Xiao Han, Yiming Ren, Peishan Cong, Yujing Sun, Jingya Wang, Lan Xu, and Yuexin Ma. 2024. Gait Recognition in Large-scale Free Environment via Single LiDAR. In ACM Multimedia 2024

    2024

  7. [15]

    Alexander Hermans, Lucas Beyer, and Bastian Leibe. 2017. In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)

    2017 arXiv

  8. [16]

    Martin Hofmann, Jürgen Geiger, Sebastian Bachmann, Björn Schuller, and Ger- hard Rigoll. 2014. The TUM Gait from Audio, Image and Depth (GAID) database: Multimodal recognition of subjects and traits. Journal of Visual Communication and Image Representation 25, 1 (2014), 195–206

    2014

  9. [17]

    Saihui Hou, Chunshui Cao, Xu Liu, and Yongzhen Huang. 2020. Gait lateral net- work: Learning discriminative and compact representations for gait recognition. In European conference on computer vision . Springer, 382–398

    2020

  10. [18]

    Zhen Huang, Dixiu Xue, Xu Shen, Xinmei Tian, Houqiang Li, Jianqiang Huang, and Xian-Sheng Hua. 2021. 3d local convolutional neural networks for gait recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 14920–14929

    2021

  11. [19]

    Weijia Li, Saihui Hou, Chunjie Zhang, Chunshui Cao, Xu Liu, Yongzhen Huang, and Yao Zhao. 2023. An in-depth exploration of person re-identification and gait recognition in cloth-changing conditions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog...

    2023

  12. [20]

    Xinzhu Li, Yi Yang, Yikun Chen, Guanghui Yue, Wei Zhou, Ruomei Wang, Xudong Mao, Juepeng Zheng, Ziqi Qiu, and Baoquan Zhao. 2025. MSPoint-Gait: Multi- Scale Point Cloud Analysis for 3D Gait Recognition via Cross-Modal Learning. In IEEE International Conference on Multimedia an...

    2025

  13. [21]

    Junhao Liang, Chao Fan, Saihui Hou, Chuanfu Shen, Yongzhen Huang, and Shiqi Yu. 2022. Gaitedge: Beyond plain end-to-end gait recognition for better practicality. In European Conference on Computer Vision . Springer, 375–390

    2022

  14. [22]

    Rijun Liao, Chunshui Cao, Edel B Garcia, Shiqi Yu, and Yongzhen Huang. 2017. Pose-based temporal-spatial network (PTSN) for gait recognition with carrying and clothing variations. In Biometric Recognition: 12th Chinese Conference, CCBR 2017, Shenzhen, China, October 28-29, 201...

    2017

  15. [23]

    Rijun Liao, Shiqi Yu, Weizhi An, and Yongzhen Huang. 2020. A model-based gait recognition method with body pose and human prior knowledge. Pattern Recognition 98 (2020), 107069

    2020

  16. [24]

    Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. 2015. SMPL: a skinned multi-person linear model. ACM Trans- actions on Graphics (TOG) 34, 6 (2015), 1–16

    2015

  17. [25]

    Phuc Nguyen, Bohyung Han, Ting Liu, and Gautam Prasad. 2018. Weakly su- pervised action localization by sparse temporal pooling network. en-US. In 2018 IEEE. In CVF Conference on Computer Vision and Pattern Recognition.(June 2018). doi, Vol. 10

    2018

  18. [26]

    Guozhen Peng, Yunhong Wang, Yuwei Zhao, Shaoxiong Zhang, and Annan Li. 2024. GLGait: A Global-Local Temporal Receptive Field Network for Gait Recognition in the Wild. In Proceedings of the 32nd ACM International Conference on Multimedia. 826–835

    2024

  19. [27]

    Yunjie Peng, Kang Ma, Yang Zhang, and Zhiqiang He. 2023. Learning rich features for gait recognition by integrating skeletons and silhouettes. Multimedia Tools Appl. 83, 3 (jun 2023), 7273–7294. doi:10.1007/s11042-023-15483-x

    2023 doi

  20. [28]

    Yunjie Peng, Kang Ma, Yang Zhang, and Zhiqiang He. 2024. Learning rich features for gait recognition by integrating skeletons and silhouettes. Multimedia Tools and Applications 83, 3 (2024), 7273–7294

    2024

  21. [29]

    Alireza Sepas-Moghaddam and Ali Etemad. 2022. Deep gait recognition: A survey. IEEE transactions on pattern analysis and machine intelligence 45, 1 (2022), 264–284

    2022

  22. [30]

    Chuanfu Shen, Chao Fan, Wei Wu, Rui Wang, George Q Huang, and Shiqi Yu. 2023. Lidargait: Benchmarking 3d gait recognition with point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 1054–1063

    2023

  23. [31]

    Chuanfu Shen, Shiqi Yu, Jilong Wang, George Q Huang, and Liang Wang. 2024. A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets, and Challenges. IEEE Transactions on Biometrics, Behavior, and Identity Science (2024)

    2024

  24. [32]

    Weijie Sheng, Xiaoyan Lu, and Xinde Li. 2023. Data augmentation by separating identity and emotion representations for emotional gait recognition. Robotica 41, 5 (2023), 1452–1465

    2023

  25. [33]

    Kohei Shiraga, Yasushi Makihara, Daigo Muramatsu, Tomio Echigo, and Yasushi Yagi. 2016. Geinet: View-invariant gait recognition using a convolutional neural network. In 2016 international conference on biometrics (ICB) . IEEE, 1–8

    2016

  26. [34]

    Torben Teepe, Ali Khan, Johannes Gilg, Fabian Herzog, Stefan Hörmann, and Gerhard Rigoll. 2021. Gaitgraph: Graph convolutional network for skeleton-based gait recognition. In 2021 IEEE international conference on image processing (ICIP) . IEEE, 2314–2318

    2021

  27. [35]

    Jiayi Yan, Shaohui Wang, Jing Lin, Peihao Li, Ruxin Zhang, and Haoqian Wang

  28. [36]

    Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, and Heng- shuang Zhao. 2024. Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. In CVPR

    2024

  29. [37]

    Cun Zhang, Xing-Peng Chen, Guo-Qiang Han, and Xiang-Jie Liu. 2023. Spatial transformer network on skeleton-based gait recognition. Expert Systems 40, 6 (2023), e13244

    2023

  30. [38]

    Zhilu Zhang and Mert Sabuncu. 2018. Generalized cross entropy loss for training deep neural networks with noisy labels.Advances in neural information processing systems 31 (2018)

    2018

  31. [39]

    Jinkai Zheng, Xinchen Liu, Xiaoyan Gu, Yaoqi Sun, Chuang Gan, Jiyong Zhang, Wu Liu, and Chenggang Yan. 2022. Gait Recognition in the Wild with Multi-hop Temporal Switch. In Proceedings of the 30th ACM International Conference on Multimedia (Lisboa, Portugal) (MM ’22). Associat...

    2022

  32. [40]

    Jinkai Zheng, Xinchen Liu, Wu Liu, Lingxiao He, Chenggang Yan, and Tao Mei. 2022. Gait recognition in the wild with dense 3d representations and a benchmark. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 20228–20237

    2022

  33. [41]

    Jinkai Zheng, Xinchen Liu, Shuai Wang, Lihao Wang, Chenggang Yan, and Wu Liu. 2023. Parsing is All You Need for Accurate Gait Recognition in the Wild. In Proceedings of the 31st ACM International Conference on Multimedia (Ottawa ON, Canada) (MM ’23). Association for Computing ...

    2023

  34. [42]

    Jinkai Zheng, Xinchen Liu, Boyue Zhang, Chenggang Yan, Jiyong Zhang, Wu Liu, and Yongdong Zhang. 2024. It Takes Two: Accurate Gait Recognition in the Wild via Cross-granularity Alignment. In Proceedings of the 32nd ACM International Conference on Multimedia. 8786–8794

    2024

  35. [2023]

    Sensors 23, 20 (2023), 8627

    GaitSG: Gait Recognition with SMPLs in Graph Structure. Sensors 23, 20 (2023), 8627

    2023

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.