Vocabulary-Guided Gait Recognition
Authors: Panjian Huang
Saihui Hou
Chunshui Cao
Xu Liu
Yongzhen Huang
Venue: NeurIPS 2025 poster ()
Keywords: Gait Recognition
Vocabulary-guided Learning
Vision-Language Models
Primary area: applications

Abstract: What is a gait? Appearance-based gait networks consider a gait as the human shape and motion information from images. Model-based gait networks treat a gait as the human inherent structure from points. However, the considerations remain vague for humans to comprehend truly. In this work, we introduce a novel paradigm Vocabulary-Guided Gait Recognition, dubbed Gait-World, which attempts to explore gait concepts through human vocabularies with Vision-Language Models (VLMs). Despite VLMs have achieved the remarkable progress in various vision tasks, the cognitive capability regarding gait modalities remains limited. The success element in Gait-World is the proper vocabulary prompt where this paradigm carefully selects gait cycle actions as Vocabulary Base, bridging the gait and vocabulary feature spaces and further promoting human understanding for the gait. How to extract gait features? Although previous gait networks have made significant progress, learning solely from gait modalities on limited gait databases makes it difficult to learn robust gait features for practicality. Therefore, we propose the first Gait-World model, dubbed $\alpha$-Gait, which guides the gait network learning with universal vocabulary knowledge from VLMs. However, due to the heterogeneity of the modalities, directly integrating vocabulary and gait features is highly challenging as they reside in different embedding spaces. To address the issues, $\alpha$-Gait designs Vocabulary Relation Mapper and Gait Fine-grained Detector to map and establish vocabulary relations in the gait space for detecting corresponding gait features. Extensive experiments on CASIA-B, CCPG, SUSTech1K, Gait3D and GREW reveal the potential value and research directions of vocabulary information from VLMs in the gait field.

Vocabulary-Guided Gait Recognition
Panjian Huang', Saihui Hou'; Chunshui Cao', Xu Liu', Yongzhen Huang 1.2 School of Artificial Intelligence, Beijing Normal University WATRIX.AI
"The way we extract gait features depends a lot on how we understand a gait."
human gait silhouette
initial contact of view angle
Gell Ma Serpaaa
VILM
3.
Wearing coot
5-
loading resporsse
Vocabulary Base
Vocabulary Space
Gait Samples
Gait Space
Vocabulary- gait recognition aims to explore gait concepts through human cabularies with VLMs where the vocabulary features enable to guide the gait network learning. Specifically, the universal vocabulary space (e.g., initial contact) can guide the gait network derive the corresponding semantic gait features, thereby yielding a more universal gait space for practicality.
Figure 1:
Abstract
What is gait? Appearance- based gait networks consider a gait as the human shape and motion information from images. Model based gait networks treat gait as the human inherent structure from points. However, the considerations remain vague for humans to comprehend truly. In this work, we introduce novel paradigm Vuuide Gait Recognition, dubbed Gait-World, which attempts explore gait concepts through human vocabularies with Vision-Language Models (VLMs). Although VLMs have achieved the remarkable progress various vision tasks, the cognitive capability regarding gait modalities remains limited. The success element in Gait- World is the proper vocabulary prompt where this paradigm carefully selects gait cycle actions Vocabulary Base, bridging the gait and vocabulary feature spaces and further promoting human understanding for the gait. How to extract gait features? Although previous gait networks have made significant progress, learning solely from gait modalities on limited gait databases makes it difficult learn universal gait features for practicality. Therefore, we propose the first Gait- World model, dubbed a-Gait, which guides the gait network learning with vocabulary knowledge from VLMs. However, due to the heterogeneity of the modalities, directly integrating vocabulary and gait features highly challenging
they reside in different embedding spaces. To address the issues, a e-Gait designs Vocabulary Relation Mapper and Gait Fine- grained Detector map and establish vocabulary relations in the gait space for detecting corresponding gait features. Extensive experiments on CASIA-B, CCPG, SUSTech1 K, Gait3D and GREW reveal the potential value and research directions of vocabulary information from VLMs in the gait field.
Introduction
Gait recognition aims to identify individuals based on walking patterns across complex covariates, e.g., cross-view and cross-clothing scenarios []] As fundamental paradigms, appearance-based "Corresponingg Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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In the silhouette modality, VLMs struggle to identify fine-grained details (e.g.. localizing the right leg), yet remain sensitive to basic walking patterns, which rely overall structure. Down: The success element Gait- World lies in leveraging gait cycle actions as the vocabularies to bridge the gap between VLMs and gait modality.
Up:
which treat gait as human shape and motion infor- mation, typically take images as input (e.g., silhouettes, parsing and optical flow) and use CNNs or Transformers to capture local and global spatio-temporal information. Model-based gait net- [ treat a gait as the human inherent structure. These methods take 17
gait networks 3 6
8
works 122 L3 15 16
points as input (e.g., keypoints, meshes, and heatmaps), and typically apply GCNs or Transformers to extract local and global relations among points and edges.
Despite the sigmificant progress made by these paradigms due to their efficiency, the gait research still faces two main problems: (i) Ambiguous gait concepts (e.g., shape, motion and structure) cause that researchers either possess ad advanced knowledge yet cannot fully apply it due to underdevcloped gait networks, or have feasible insights but cannot verify whether the network truly works as intended. (ii) Constrained gait networks rely solely on the gait modality and the limited gait databases, struggling to learn universal features for the real-world scenarios. For example, appearance based methods suffer from drastic appearance variations under cross-clothing conditions, model-base methods heavily depend on the accuracy of upstream pose estimators, and gait databases often encounter sparse-view [19 and cloth-imbalance (20) problems. Therefore, we naturally ask: What is a gait and how to extract gait features? Considering that human vocabulary inherently possesses interpretability and semantic guidance, we introduce new gait paradigm and network to answer:
Vocabulary-guidedd gait recognition. The primary goal is harness human -effned vocabulary with Vision-Language Models (VLMs) to explore gait concepts, thereby promoting gait networks and providing researchers more informative feedback. Inspired by "The limits of my language mean the limits of my world." from Ludwig Wittgenstein, we name the paradigm Gait-Worlh shown in Figure [ Gait- World consists of Vocabulary Base, VLMs, and Gait Network. Researchers provide basic knowledge as the Vocabulary Base, from which VLMs extract the vocabulary features to serve as priors that guide the gait network learning corresponding gait features. However, integrating human vocabulary knowledge into gait modalities via VLMs is non-trivial because publicly available training data for VLMs rarely include gait samples. As shown in Figure 2
VLMs (i.e., GPT-4c") often struggle to identify tme-graaied details (e.g. hands or legs) in silhouettes. Nevertheless, we observe phenomenon where VLMs remain sensitive to basic walking patterns. We adopt the clinically defined eight-phase gait cycle as a minimal, complete set [21 22). and VLMs accurately recognize gait cycles from silhouettes, aligning with "Gait serves as a walking descriptor." Therefore, the success element in Hait-World is the proper vocabulary prompt where this paradigm carefully selects gait cycle actions as Vocabulary Base, bridging the gait and VLM spaces and further promoting human understanding for the gaits.
o--Gait. Towards vocabulary-guin guided gait recognition, we introduce the first Sait-World model, a-Gait, which leverages vocabulary knowledge from VLMs to guide gait representation learning. Specifically,
https://openat.com/

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due to the modality heterogeneity, directly integrating vocabulary and gait features poses challenge, as they reside in distinct embedding spaces. To address this, a-Gait firstly employs the Vocabulary Relation Mapper that maps the vocabulary feature into the gait space and establishes vocabulary relations. Then, the Gait Fine- Fine-grained Detector queries the gait features with the vocabulary guidance, extracting corresponding semantic gait features for recognition.
Our main contributions can be summarized as follows:
We introduce novel paradigm Vocabulary-Guided Gait Recognition, dubbed Gait-Worke which applies human vocabularies with Vision-Langusge Models to effectively explore gait concepts, revealing the vocabulary value for the gait field.
We propose G-Ga in pursuit of the Gait-World, which designs Vocabulary Relation Mapper and Gait Fine-grained Detector to map and establish vocabulary relations into the gait space, effectively tackling modality heterogeneity and refining gait features.
We evaluate a-Gait on CASIA-B, CCPG, SUSTech1K, Gait3D and GREW, achieving superior performance and providing valuable insights.
2
Related Work
2.1 Gait Recognition
Model-Based Gait Recognition. PoseGait [12 lifts 2D images to 3D poses and learns spatio- temporal cues with a multi-loss scheme for robustness. GaiiGraph/GaiiGraphh2 [I3] 23 use GCNs on 2D pose sequences to model motion while reducing appearance sensitivity. GaitTR [14] couples spatial transformers with temporal convolutions. GaitMixer [24 mixes spatial self-attention with large-kernel temporal convolutions. GPGait [15] improves generalization via unified pose represen- tation. SMPLGait [16] encodes shape and motion with dense 3D body models. SkeletonGait [17), HiH [25] and GaitHeat [88 use Gaussian-style maps strengthen structural cues.
Appearance- Gait Recognition. GaitSet 2 views silhouettes as unordered GaitPart
exploits part-wise signals. GaitGL 4 combines local and global 3D convolutions. GaitBase 5 is
simple, strong foundation for in-the-wild use. DANet (6]. DyGait (26), HSTL (27], VPNet 7
GLGait (28). and GaitMoE [1O] emphasize dynamic modeling. GaitGCI 29 GaitCSV [9]
CLTD (30,, and G (31 apply causal inference to curb covariate effects. Origins 332 leverages
generative diffusion to mitigate semantic inconsistency and uniformity. Beyond silhouettes, parsing- based inputs (GaitParsing [33], LandmarkGGii 34,, ParsingGait (35)) capture fine- -raiind parts. RGB
pipelines (GaitEdge (36), BigGait (37)) enable end-to-end learning. point clouds (LidarGait (38))
address occlusion. multi-modal designs (MMGaitFormer (39), CL-Gait [40]) enrich cues. and
Gait-X 411 builds X-modality via patch-wise DCT for stronger in-/cross-domain performance.
Vision-Language Models
Gait World derives its vocabulary space from a Text Encoder built on either Vision-Language Models (VLMs) or purely textual Large Language Models (LLMs).
VLMs. (i) CLIP 42): contrastive image-text embeddings enabling broad zero-shot transfer and prompt-based retrieval/classiffcation (ii) LLa VA [3): CLIP-style visual encoder connected to an LLM via lightweight adapter for instruction-following multimodality. (iii) Qwen [44]: Qwen-V VL supports multi-image. high-resolution inputs with strong captioning and grounding for fine-grained semantics. (iv) GPT [5 GPT-4V (and 40) accepts images for VQA and multi-ster reasoning over visual content.
widely used 7B-65B base family for downstream NLP adapters and tools. (ii) GPT 45 GPT-3/4 exhibit in-context learning, instruction following, and strong general reasoning text-only settings. (iii) DeepSeek [47] V3/R1 emphasize efficiency and explicit reasoning with MoE and RL-style training, improving coding and mathematical tasks.
LLMs. (i) LLaMA
3

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2.3 Yocabulary-Guided Learning
Using an explicit vocabulary links visual evidence to linguistic semantics. We outline two representa- tive directions: Open-Vocabulary Learning and Text-Guided Learning.
Open-Vone Vocabulary Object Detection. The goal to detect objects beyond fixed training label set by transferring language-aware knowledge. ViLD 488 distills a VLM teacher into a region-based detector generalize to unseen categories. Detic [49 adds image-level supervision from large-scale VLM pretraining so one model handles both in- and uut-of-vocabulayy classes. OV-DETR [50] couples a transformer detector with linnon- pretraining, predicting categories directly from text embeddings.
Text-Guided Face Recognition. Text serves as guidance supervision to refine identity features across granularities. CFAM [31] aligns images and captions at multiple resolutions. CaptionFace [52] combines a GPTFace component with a multi-scale feature alignment module. TGFR [53] uses
cross-modal contrastive learning over global-loca, face-caption pairs.
Discussion. Vocabulary-Guided Gait Recognition instead uses vocabulary priors to query identity- relevant gait cues. Unlike open-vocabulary detectors that treat words as labels, and text-guided face recognition where VLMs plug in directly, current VLMs are not yet sensitive to gait, requiring additional alignment.
3
Methodology
In this section, we first present the formulation of vocabulary-guided gait recognition in Sec. 3.1 then offer comprehensive description of o-Gait Sec. Sec.3.2 followed by the training and inference details in Sec. 3.3 Finally, we discuss various aspects of this work in Sec. 3.4
3.1 Docabulary-Guided Gait Recognition
We begin with the gait silhouette modality and appearance-based gait network for the simplicity and efficiency. A vanilla gait framework typically takes a silhouette sequence X' as input, then extracts gait features using a Gait Encoder & Next, Horizontal Partitioning 'P is applied to obtain fine-grained gait part features O, which are finally mapped to for recognition through a Gait Head G. This process can be follows:
O P(E(N))
(1)
F = C(O)
(2)
where X' E RRSHXX O E RCxxP E RCxX and Cn, S, H. W, represent channel, consecutive S frames, height, width and the number of horizontal parts. This process relies learning solely from gait modalities on limited gait databases, which makes it difficult to learn universal gait features.
To address this, introduce Vocabulary-Guided Gait Recognition, dubbed Gait-World, which leverages vocabulary information from VLMs for better human understanding and gait semantic guidance. Gait-World comprises Vocabulary Base V. VLMs T, and Gait Network. Because VLMs are insufficiently sensitive to gait silhouette modality, we prepend the qualifier "human gait silhouette" to all vocabularies, which provides more precise descriptions for VLMs (e.g., "human gait silhouette initial contact"). For convenience, this qualifier is omitted in subsequent discussions. Next, we provide more details for Gait-World which mainly consists of three components:
Vocabulary Base. We predefine Vocabulary Base consisting of ("initial contact", "loading response", "mid stance", "terminal stance", "pre-swing", "initial swing", "mid swing". "terminal swing") J. As Ssaoes/ the selection is motivated by the observation that VLMs are sensitive to the gait cycle actions that depend the global details of gait silhouettes, but less sensitive to local details (e.g., distinguishing legs from the silhouettes). Therefore, VLMs can accurately determine the phase of the gait cycle, which is crucial for gait recognition. In practice, Cait-World uses the Vocabulary Base to bridge the gap between the VLM and the gait spaces.
VLMs. Gait World aims to hamesss the capacity of Vision-Language Models (e.g., CLIP) or Large Language Models (e.g., DeepSeek R1), which embody rich and universal knowledge. Specifically, the Text Encoder trained on large-scale public data develops vocabulary space that generalizes well

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Vocabuaary Base
5X X
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Figure 3: a-Gait employs the vocabulary features from Text Encoder to guide the gait feature learning. HP denotes Horizontal Partition. Gait Encoder consists of several convolution blocks, and Gait Head includes Separate FCs and BNNeck. After the gait sequence passes through Gait Encoder and HP, the gait part features are fed into the Vocabulary Relation Mapper and Gait Fine-grained Detector, which guide them with vocabulary information for identification.
real- world scenarios, which enables to guide the feature learning in the Gait Network. This process can be as follows:
Vy T(V)
where Vy E RCXXXXM and C.. C and M denote channel, the number of tokens and the number of vocabulary. Note that the number of tokens assigned each vocabulary feature can vary due to the differences in lexical length and tokenizer segmentation. For example, "pre-swing" and "initial contact" are tokenized into and 2 tokens, respectively. Therefore, we employ the simple yet effective Mean Pooling (MP) to aggregate multiple tokens within sentence into one token, i.e., V/ E RC x.M
Gait Network. The vocabulary features from VLMs serves external information for guiding gait network learning. Therefore, the Gait World paradigm is not constrained by current gait modalities or networks where vocabulary information solely guides gait representation learning.
3.2 Model Architecture
Towards Vocabulary- Guided Gait Recognition, we propose the first-generation model a-Gait in this series, which primarily incorporates vocabulary information after the Gait Encoder and Horizontal Partitioning (i.e., gait part features) due to the Vocabulary Base with temporal information and gait recognition with the fine-grained problem. Based Gait-World, o-Gait introduces the Vocabulary Relation Mapper (VRM) and the Gait Fine-grained Detector (GFD), addressing the discrepancies between the vocabulary and gait feature spaces, as well as the lack of universality of gait features.
Vocabulary Instruction. As shown in Figure 3 a-Gait firstly obtains the vocabulary features Vy E X. from Text Encoder. Note that gait cycle actions are inherent attribute shared by all individuals and rely on the overall structure in silhouettes. Therefore, vocabulary features are shared across the body parts of all gait samples.
a- Gait extracts the gait part feature O RC,x8xP from Gait Encoder & and Horizontal Partitioning P. preserving the temporal information for vocabulary guidance. Note that each guit part feuture contains the distinct walking pattern. Therefore, both Vocabulary Relation Mapper and Gait Fine- grained Delector are independent for each gait part feature, and omit the part index for simplicity. Given the vocabulary features V, E RCxXM and one gait part feature O E pC XS the VRM and GFD are as follows:
Vocabulary Relation Mapper. Although Gait- carefully selects Vocabulary Base that highly relevant to gait, there remains significant feature distribution discrepancy between the Text Encoder and Gait Encoder. This discrepancy arises mainly for two reasons. Firstly, the Text Encoder modeling paradigm is based on sequential processing of the text modality. Secondly, the Text Encoder learning framework primarily relies on autoregressive Next-Token Prediction or Contrastive Learning from RGB Image-Text pairs. To address these issues, VRM firstly introduces Transition module to align vocabulary features into the gait space, and then Relational Query On with the attention mechanism to establish associations among vocabularics, which enables the gait network to understand the
5

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vocabularies in more fine-graines manner. The process is as follows:
V ReLU(LN(Linear(()))
(4)
Qn Qn. KR=Vž V',, VR = V/ Qy = SSotmax Qn @ KR) & VR
(5)
(6)
where Linear RCixC2 QR RC,X1 Kn E RCxMM, VR IRCxMM, Qy E RGXX VRM
eliminates the linear mapping in the attention mechanism to preserve the original vocabulary feature distribution as much as possible. Moreover, VRM normalizes the aligned vocabulary feature Qv for
guiding gait feature learning.
Gait Fine-grained Detector. Within the Gait Gail-World paradigm, GFD primarily achieves that the vocabulary feature Qv guides the gait feature learning with the corresponding semantic information for the complex real-world scenarios. To this end, GFD treats the process from an object detection perspective where the vocabulary feature detects gait features with the corresponding semantics. Similar to the DETR [54], GFD presents Qy as the object query and the gait part feature O as the regions of interest. The process is as follows:
Qv = =Qv. Kv= W =0
F Softmax (Ωv @ kv) @ Vv
(8)
where Qv E RR2x11, Kv E TRC,xS Vy E TRC, F RC,x1, GFD) also normalizes the detected gait feature F for the following Gait Head, easing the training process.
3.3 Training Details
Training Stage. a-Gait aims to recognize the individual identity where vocabulary information from VLMs solely guides the gait feature extraction. Consequently, a- o-Gait remains consistent with conventional gait recognition, including two types of identity loss. Triplet Loss [5 Ltp and Cross Entropy Loss Lees constraining each part independently.
== Cip Lee
Inference Stage. A After training, strong relation established between the Vocabulary Base space from VLMs and the gait space. At the inference stage, a-Gait remains the Vocabulary Base to refine gait features for real-world scenarios.
3.4 Discussion
To facilitate clear grasp and significance of (ocabulary-Guide Gait Recognition, we further explain and clarify Gait- World, a-Gait and the scope this work:
Gait-Worke World aims to provide a better human understanding of gaits, and new paradigm to com- plement existing paradigms (i.e., appearance- based and model-based methods). It serves as an intuitive and efficient tool for researchers to refine their understanding of gait patterns, providing new directions for gait research. Researchers only need to design better vocabulary prompts and share their embeddings with the gait community, without the burden of computational and memory overhead introduced by large models or the need for complex gait model architectures.
a-Gait serves as an initial attempt, which aims to provide an intuitive demonstration of the vocabu- lary's effectiveness for gait recognition. Hence, we propose simple yet effective architecture, rather than relying on complex frameworks for incremental performance gains.
The relationships with multimodals. Multimodal approaches typically require each input to be paired with the corresponding several modalities, which, despite offering information gains, also introduces challenges in data collection and computational overhead. o-Gait serves initial attempt with only eight universal vocabulary embeddings shared across all inputs, and admittedly does not yet constitute multimodal paradigm.
4 Experiments
The mainstream public gait databases and the implementation details are shown in Appendix and the evaluations on method are introduced in the next sections.

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Table 1: The evaluation on CCPG with clothing-changing conditions.
Gait Evaluation Protocol
ReID Evaluation Protocol
Parudigm
Method
Venue
CL
UP
DN
BG
Mean
CL
UP
DN
BG
Mean
GaitGraph2 13
CVPRW22
5.0
5.3
5.8
6.2
5.1
5.0
5.7
7.3
8.8
6.7
Gait-TR 14
ES23
15.7
18.3
18.5
17.5
17.5
24.3
28.7
31.1
28.1
28.1
Model
MSGG 36
MTA23
29.0
34.5
37.1
33.3
33.5
43.1
52.9
57.4
49.9
50.8
SkeletonGait
AAAI24
40.4
48.5
53.0
61.7
50.9
52.4
65.4
72.8
80.9
67.9
GaitSet
AAAII9
60.2
68.5
64.8
77.5
85.0
82.9
83.2
GaitPart
CVPR20
64.3
68.6
71.7
68.1
79.2
85.3
86.5
84.8
Appearance
OGBase 57
CVPR23
52.1
60.1
63.3
58.2
70.2
76.9
80.4
77.7
GaitBase 6
CVPR23
75.0
76.8
78.6
75.5
88.5
92.7
93.4
92.0
DeepGaitV2 V2
58
TPAMI25
78.6
80.7
89.2
83.3
90.5
96.3
91.4
96.7
93.7
Gait-Worke
o-Gait-S (ours)
NeurIPS25
82.8
89.0
84.6
92.7
87.3
92.0
98.1
93.4
96.9
95.1
Table 2:
The evaluation on SUSTech with different attributes (abbrev.: NM=Normal, BG=Bag, CL=Clothing, CRY=Carrying, UMB-Umbrella, UNI=Uniform, OCC=Occlusion, NT=Night).
Probe Sequence
Overall
Paradigm
Method
Venue
NM
BG
CL
CRY
UMB
UNI
OCC
NT
Rank-1 Rank-5
GaitGraph2 [33
CVPRW22
22.2
18.2
6.8
18.6
13.4
19.2
27.3
16.4
18.6
40.2
Gait-TR 14]
ES23
33.3
31.5
21.0
30.4
22.7
34.6
44.9
23.5
30.8
56.0
Model
MSGG 56
MTA23
67.1
66.2
35.9
63.3
61.6
58.1
66.6
17.9
33.8
SkeletonGait 77
AAA124
67.9
63.5
36.5
61.6
58.1
67.2
79.1
50.1
63.0
83.5
GaitSet
AAAII9
69.1
68.2
63.1
23.0
65.0
84.8
GaitPart
CVPR20
62.2
62.8
33.1
59.5
57.2
54.8
57.2
21.7
59.2
80.8
Appearance
GaitGL 4
ICCV21
67.1
66.2
35.9
63.3
61.6
58.1
66.6
17.9
63.1
82.8
GaitBase S
CVPR23
81,5
77.5
49.6
75.8
75.5
76.7
81.4
25.9
76.1
89.4
DeepGaitV2 58
TPAM125
87.4
84.1
53.4
81.3
86.1
84.8
88.5
28.8
82.3
92.5
o-Gait-S (ours)
NeurIPS25
91,1
87.2
64.0
85.3
89.5
88.8
92.7
86.3
93.9
4.1 Results Constrained Scenario
CASIA-B. As shown in Table a-Gait-T achieves competitive performance under all conditions, with an average accuracy of 94.8%, proving the universality of gait cycle action under NM, BG, and CL scenarios. Specifically, a-Gait-T approaches the SOTA on NM (98.9 %) and BG (96.8%).
CCPG. As shown Table I a- g-Gait-S significantly outperforms appearance- based and model-basee methods in the more challenging full-body clothing change scenarios. For instance, it exceeds DeepGaitV2 by 4% in mean accuracy, demonstrating that textual information can better guide the model in learning covariate-independenn features.
4.2 Results on In-the-wild Scenario
In real-world scenarios, such as occlusions, umbrella usage, and varying lighting conditions, a-Gait-T significantly surpasses previous SOTA methods shown in Table For example, it outperforms DeepGait by 4% in Rank-1 accuracy, demonstrating the feature robustness of the text-guided gait cycle actions.
SUSTech1K.
Gait3D. In larger-scale scenarios, although silhouette-basee methods are approaching saturation due to the impact of covariates on upstream segmentation algorithms, a-Gait-M is still able improve performance shown in Table For instance, it surpasses GaitMoE by 2.6% in Rank-1 accuracy, valuable complement to existing approaches.
indicating that the Gait- World paradigm
GREW. Similarly, GREW is also significantly affected by upstream gait modal extraction algorithms, with silhouette-based methods approaching the limitations. As shown in Table 4 o-Gait-L achieves achieves competitive results, surpassing VPNet (7) by 1. 1.2%. a-Gait-L. adopts Free Lunch 61 (i.e., logits as
gait features) to achieve more stable results without introducing additional computational complexity.

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Table 3: The evaluation on CASIA-B under different conditions with Rank-1 I accuracy (%).
Paradigm
Method
Venue
NM
BG
CI.
Mean
GaitGraph2
CVPRW22
80.3
71.4
63.8
71.8
Model
GaitTR 14
ES23
94.7
89.3
86.7
90.2
GPGait
ICCV23
93.6
80.2
69.3
81.0
GaitSet
AAAI19
95.0
87.2
70.4
84.2
GaitPart
CVPR20
96.2
91.5
78.7
88.8
GLN 39
ECCV20
96.9
94.0
77.5
89.5
GaitGL
ICCV21
97.4
94.5
83.6
91.8
QAGait 60
AAA124
97.9
94.6
78.2
90.2
GaitBase
CVPR23
97.6
77.4
89.8
DANet
CVPR23
98.0
89.9
94.6
Appearance
GaitGCI 29
CVPR23
97.9
95.0
86.4
93.1
DyCiit
ICCV23
98.4
96.2
87.8
94.1
HSTI 227
ICCV23
98.1
95.9
88.9
94.3
VPNet 7
CVPR24
98.3
96.3
90.0
94.9
DeepGaitV2 558
TPAMI25
89.6
CLTD 30
ECCV24
98.6
96.4
89.3
94.8
Free Lunch 61
ECCV24
98.1
94.1
77.9
90.0
Sait-World
o-Gait-T (ours)
NeurlPS25
98.9
96.8
88.6
94.8
Table 4: The evaluation on Gait3D and GREW.
Gait3D
GREW Rank-5
Paradigm
Method
Venue
Rank-1
Rank-5
mAP
Rank-1
Rank-10
GaitGraph22
CVPRW22
11.2
64.8
Model
GaitTR 4
ES23
7.2
48.6
GPGait
ICCV23
22.4
57.0
GaitSet
AAAI19
36.7
30.0
46.3
63.6
70.3
GaitPart
CVPR20
28.2
47.6
47.6
44.0
60.7
67.3
GaitGL
ICCV21
29.7
48.5
22.3
47.3
63.6
MTSGait 62
MM22
48.7
67.1
37.6
55.3
71.3
76.9
QAGait 60
AAA124
67.0
81.5
56.5
59.1
74.0
79.2
GaitBase S
CVPR23
64.6
60.1
GaitGCI 29
CVPR23
50.3
68.5
39.5
68.5
80.8
84.9
Appearance
DyGait 26
ICCV23
66.3
80.8
56.4
71.4
83.2
86.8
HSTI.
ICCV23
61.3
76.3
55.5
62.7
76.6
81.3
VPNet 7
CVPR24
75.4
87.1
80.0
89.4
DeepGaitV2 58
TPAMI2S
74.4
88.0
65.8
77.7
88.9
91.8
CLTD 30
ECCV24
69.7
85.2
78.0
87.8
GaitMoF: 10
ECCV24
73.7
66.2
79.6
89.1
Free Lunch 61
ECCV24
70.1
61.9
65.5
78.7
83.3
Gait-Worl
o-Gait-M/L. (ours)
NeurIPS25
76.3
87.7
67.8
81.2
90.2
92.7
4.3
Ablation Study
In this section, validate the universality of the Gait-World with different Text Encoder, and illustrate the modality and vocabulary expansions. Additionally, we visualize the mechanism of vocabulary guidance, and analyze the trade-off of c-Gait between accuracy and efficiency.
The effectiveness Gait world...... shown Table although Gait Network relying solely on gait silhouettes and adaptive learning achieves competitive results, under the Gait-World paradigm, o-Gait further improves Rank-1 accuracy by 2.6% on Gait3D. This indicates that the vocabulary space distribution derived from Large Language Models possesses greater universality.

---

Table 5: The ablation study on Gait3D and CCPG.
Gait3D
CCPG
Method
Rank-1
mAP
CL
UP
DN
BG
Mean
a-Gait
67.8
82.8
89.0
84.6
92.7
Gait Encoder
73.6
65.1
80.1
86.4
83.9
91.4
85.5
The analysis
Text Encoder
Initial Random
71.8
63.3
77.7
84.2
77.6
86.2
81.4
Learnable Query
74.1
66.8
82.3
89.0
83.9
92.5
86.9
CLIP
75.2
67.7
82.2
88.9
85.0
92.8
87.2
LlaMa
74.8
66.9
82.5
89.3
84.3
93.0
87.3
DeepSeekRI I
76.2
67.8
82.8
89.0
84.6
92.7
87.3
Table 6: Ablations modality and vocabulary expansions Gait3D
Paradigm
Variant
Vocabulary Basc
Rank-1
mAP
Gait Encoder
73.6
65.1
Appearance
Gait Encoder w/ a-Gait
[8 phases]
76.2
67.8
SkeletonGait
29.6
Model
SkeletonGait a-Gait
phases]
40.3
31.3
SkeletonGaitt+
76.0
69.2
Multimodel
SkeletonGait++ w/ a-Gait
[8 phases]
78.1
71.5
a-Gait
phases]
76.3
67.8
Gait-World
o-Gait w/ More vocahularies
phases view, hag. elothing inv.]
76.8
68.4
The analysis on Text Encoder. To analyze the importance of vocabulary features, first replace them with Initial Random features. As shown in Table the results show that non-rclational features disrupt gait learning, thereby validating the effectiveness of a-Gait from the benefits of strongly associated vocabulary features rather than the architectures (i.e., VRM and GFD). Additionally, we replace with Learnable Query, which starts from normal distribution but adapts through VRM and GFD to learn relevant information from the gait features, confirming the architecture 's efficiency. Furthermore, substituted different Text Encoders, leading to different improvements. DeepSeek- RI-Distill, with its universal vocabulary reasoning capabilities, produced superior vocabulary features, while CLIP, leveraging image-text pairs for alignment, excelled in capturing visual features.
The modality and vocabulary expansions. shown in Table 6 vocabulary guidance brings consistent gains. For the Appearance-based method, adding a-Gait lifts Lank-1/mAL from 73.6/65.1 to 76.2/67.8 (+2.6/+2.7), indicating that phase-aware cues complement generic silhouette features. For Model-based method, SkeletonGait improves from 37.9/29.6 to 40.3/31.3 (+2.4/+1.7), showing larger benefits when the baseline weaker. In Multimodel- increases from 76.0/69.2 to 78.1/71.5 (+2. 1/+2.3,,, meaning the guidance remains effective with stronger backbones. Within World, expanding beyond the eight phases with view-angle, bag, and clothing invariants brings further rise from 76.3/67.8 to 76.8/68.4 (+0.5/+0.6), consistent with the goal of suppressing appearance confounders.
method, SkeletonGait++ also
The visualization of vocabulary guidance. We provide qualitative analysis to validate that a-Gait extracts gait fcatures related to vocabulary information and provide meaningful feedback for humans. As shown in Figure 4 given the eight gait cycle vocabularies, the highest Softmax response in the attention mechanism. It can be observed that GFD accurately captures gait cycle actions under various covariates, revealing that the a- e-Gait indeed understands the human vocabulary. Meanwhile, it also inspires researchers to better understand gait.
visualize the silhouettes with
The efficiency-
trade-off. As shown in Figure 5] a-Gait (59.1 M params, 85.6G FLOPS)
reaches 76.3% accuracy, sitting on frontier of this cohort. Versus DeepGait (25.5 M 85.3 G/ 74.4%), it uses nearly the same compute (+0.3 G FLOPs) yet improves accuracy by 1.9% by steering attention shase-specific adentity-bearing cues via vocabulary-gul -uidddd detection. Relative to DyGait (133,1 M/ G 66.3%), (i),, it is lighter by 74.0 M parameters and 153.4 G FLOPs while improving

---

X
K
Figure The gray box represents the complete gait sequence. Give the eight gait eycle vocabularies, GFD detects the eight silhouettes with the highest Softmax response in the attention mechanism, shown in the dash boxes.
80
a-Gait (Ours, 76.3 %) M,
DyGait (ICCV. 66.3 133.1 M. 239.0 G
Vaip (TPAMI, 74.4 %)
70
25.5 M, 85.3G
HSTL (ICCV, 54.7 M, 29.7 G
60
GaitBase (CVPR. 64.6
GuaSe: (AAAI, 36.7 ().
19.3 M, 35.4
GsaPann 28.2 5)
Rank-1 (%)
GaaGL (ICCV, 29.7 16)
EMPLGait (CVPR, 46.3 %)
SMPLGai (CVPR. 46.3 %)
50
30.2 M, 13.4 G
GalBaae (CVPR.
DyGait (IOCV, 66.3 %)
OCCV 61.3 5))
GaitSet (AAAL 36.7 %) 6.3 M. 12.9 G
DeepCaitV2 (TPAMI, 74.4 %)
40
e-Gai (Curs, 76.3 76)
GaitGL (ICCV, 29.7 %
GaitPart (CVPR, 28.2 4,8 M. 7.9
60.3 M, 58.6 G
30
20 0
50
100
150
Params. (M)
Figure Model comparison on Gait3D: Rank-1 (%) vs. parameters (M) and FLOPs (G).
accuracy by 10.0%, VRM-based alignment injects human-understandable gait terms that suppress clothing/view confounders and reduce the search space. At training and inference stages, the text encoder frozen and word embeddings are cached offline, adding negligible runtime overhead.
5 Conclusion and Limitations
In this work, we introduce the vocabulary to the gait field due to the inherent interpretability and semantic guidance. Specifically, we propose novel paradigm Gait-World, which aims explore gait concepts with human vocabulary and VLMs. Gait-World integrates vocabulary information into the Gait Network by leveraging gait cycle action vocabularies, thereby enhancing human understanding of gaits. Furthermore, we introduce o-Gait, the first model under the MaitdWorld paradigm, which utilizes VRM and GFD to more precisely guide gait feature learning with corresponding vocabulary features. Extensive experiments multiple complex gait databases prove the universality.
Limitations and Future Works. a-Gait serves an initial attempt with eight universal vocabulary embeddings preliminarily validates the value of vocabulary information for gait recognition, whereas the more comprehensive exploitation of vocabulary information yields richer benefits, such as gait attribute learning with the vocabulary labels. In future work, it can be extended to be multimodal paradigm, providing each input with unique language description, enabling richer gait features. Additionally, detailed discussion of risks and safeguards is provided in Appendix B In conclusion, Gait- World provides better human understanding of gaits, and a new paradigm to complement existing paradigms.
10

---

Acknowledgement
This work is jointly supported by National Natural Science Foundation of China (62276025, 62206022, 62476027) and the Fundamental Research Funds for the Central Universities (2253200026).
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14

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A Databases and Implementation Details
Table 7: Id. and Seq. denote the number of identities and sequences. CV, BG and CL refer to cross-view and carrying bags and cross-clothing conditions. D and C denote the number of cony blocks and the channels in each visual stage.
Environment
Dataset
Id.
Train
Seg.
ld.
Test
Seq.
Condition
Stage [Di, Dr, Da. Del
[Ci, Co. Ca. Cr|
Channels
Strides
Constrained
CASIA-B 63
74
8,140
50
5,500
CV. BG, CL
1.
164, 128, 256,
[1. -
CCPG [57
100
8,187
8,095
CV. BG,
11111.11
64, 128, 256, 512]
1,, 1]
SUSTech K 38
200
5988
850
19.228
Real-world
1.
664 128. 256. 512]
[1.
In-the-wild
Gait3D T6
3,000
18,940
1,000
6,369
Real-world
11, 11,444111 11
664 128, 256, [1,] 2.2.
GREW 64
20.000
102,887
6.000
24.000
Real-world
12.4.4.2)
64, 128, 256, [12]
[1,
A.1
Databases
Gait databases commonly categorized into groups: Constrained and In-the-wild scenarios. As
shown in Table [ CASIA-B(63), CCPG (57) generally include fewer individuals but provide explicit condition types. In-the-wild databases SUSTech K (38), Gait3D [T6] and GREW [64] contain larger number of identities and more challenging scenarios (e.g., occlusions).
CASIA-B 63 includes 124 subjects recorded from 11 view angles, which contains Normal Walking (NM), Carrying Bags (BG) and
(CL) conditions.
CCPG [77 concentrates on the effects of clothing variations, including 200 individuals with more than 16,000 sequences. By providing fine-graine ine-grained clothing variations and realistic challenges, CCPG helps researchers investigate how to handle cloth-changing issues more effectively.
SUSTechIK [38] is a large-scale, multimodal gait dataset collected by LiDAR sensor and an RGB camera. It comprises 1,050 subjects, including diverse real-world conditions (e.g., clothing, night-time, and view angles scenarios).
Gait3D [6] is a large-scale gait database collected from 39 cameras in a supermarket with factors like occlusions and view angles. It includes 3,000 subjects, divided into a training subset of 2,000 and a testing subset of 1,000.
GREW 64 is a large-scale in-the-wild database comprising 26,345 subjects and 128,671 sequences collected from 882 cameras. Each sequence provides rich modalities, silhouettes, optical flow, and 2D/3D pose, enabling both appearance-basee and model-based gait studies. The 20,000 subjects are designated for training and 6,000 for testing, and each test subject contributes two gallery sequences and two probe sequences.
A.2 Implementation Details
We describe the training process below in detail:
Inputs.
The silhouettes on all databases are transformed into 64 x 44, and each sequence consists of 30 consecutive frames. We adopt the mini-batch [Z, J] is consistent with (5), and I, J denote the number subjects and the number of sequences, respectively.
Networks. We provide four model types: o-Gait-T, c--GaitSS, o-Gait-M, a-GaittL, improving the optimization on different-scale databases. All models employ the Stem module and 2D ResBlock in the first Stage, which is consistent with DeepGaitV2 o-Gait-T consists of 3 Stages with block numbers [1, 1, 1]. channels [64, 128, 256], where the Bottleneck blocks place in the last 2 Stages. a-Gait-S consists of Stages with block numbers [1. 1, I. 1]. channels [64, 128, 256, 512]. where the Bottleneck blocks place in the last 3 Stages. a-Gait-M consists of 4 Stages with block numbers
[1, 4, 4, 1]. channels [64, 128, 256. 512], where the P3D blocks place in the last 3 Stages. a-Gait-L consists of 4 Stages with block numbers [2, 4, 4, 2], channels [64, 128, 256, 512], where the P3D blocks place in the last Stages.
Optimization. We employ SGD with an initial learning rate of 0. which is reduced by 0.1 at specific iteration milestones where CASIA-B, CCPG, SUSTech1 Gait3D and GREW are [20K] 50K), [20K, 40K, 50K], [20K, 30K, 40K), [20K, 40K, 40KK 50K] and [80K, 180K,120 120K, 150K), respectively. The total training iterations of CASIA-B, BB CCPG, SUSTechIK, G and GREW are 60K, 60K,
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50K, 60K, 180K, respectively. Text Encoder. We select CLIP, LlaMa3-8B. and DeepSeek-Re -11 Distill-Llama--00 representative Vision-Language Models (VLMs) to validate the effectiveness of Gait-World.
B Responsible Use, Risks, and Safeguards
Scope.
This work studies vocabulary-guider gait recognition under a research-only setting. All experiments public datasets approved for academic use and silhouette/skellton representations (no RGB/audio).
Risks. (1) Covert or indiscriminate surveillance; (2) use without informed consent; (3) unfair errors across -obulluulaaion: (4) function creep beyond the stated research scope.
Technical safeguards.
Release silhouettes/skelltons only; prohibit identity recovery and real-time CCTV deploy- ment without explicit, informed consent.
Freeze text encoder and cache vocabulary embeddings at inference, keeping guidance overhead minimal and auditable.
Provide subgroup reporting (e.g., gender/agc/assiitive devices); if disparity exceeds preset threshold, retrain with c--wiighiig/faimees regularizers and document the outcome the model card.
Process and access controls.
Nomecmmmerlaal, research-only license forbidding surveillance use; usage must document consent or a clear legal mandate.
Gated access with per-request approval: logged usage; immediate revocation and public disclosure upon policy breach.
Incident response: if any identity is recoverable, notify within 48 h and irreversibly delete the recovered data/models.
Scope limitation. The system is intended for academic benchmarking and analysis; deployment in operational surveillance identification systems of scope.
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Reviews:
[{"review_id": "rv9LJyrk4a", "reviewer": "Reviewer_VrEo", "summary": "This paper has a new idea. It uses words about walking, like 'initial contact' or 'mid swing', to help computers recognise people from their walk (gait). They call this new method Gait-World. Their model, α-Gait, takes these words from a Vision-Language Model (VLM) and uses them to guide the main gait recognition network. This helps the network focus on the right parts of the walking style. They tested it on many datasets like CASIA-B, CCPG, and GREW, and showed that their model works very well, even better than many old methods.", "questions": "1. Your Vocabulary Base has only eight phrases, which are the technical terms for a gait cycle.  Why did you choose only these? Did you try using more simple or descriptive words like \"walking fast\", \"limping\", or \"carrying a bag\"?\n2. α-Gait model uses a Text Encoder which is a big model.  How much does this slow down the recognition time compared to a normal model like GaitBase? Can you give some numbers on inference speed?\n3. Your results on the CCPG dataset are much better than other methods , but on CASIA-B the improvement is not so big.  Can you explain why your method works so well for clothes-changing but is only competitive in normal view-changing scenarios?\n4. Gait recognition is a biometric technology and can be used for surveillance. This has a negative societal impact. Can you please add a discussion about these risks and how they can be managed?", "limitations": "No. The authors have a small section on limitations , but it is only about future technical work, like using more words or making it a multimodal system.  They do not at all discuss the main limitation which is the potential negative societal impact of gait recognition technology (e.g., surveillance). This is a serious miss.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n\nOriginality is very high. The idea to use human vocabulary to guide gait recognition is very new. I have not seen this before. It connects two different fields, language and vision, for this problem.\n\nSignificance is good. This paper opens a new way to do research in gait recognition. Other people can now try using different words or better language models to improve results.\n\nQuality of experiments is strong. The authors tested their model α-Gait on five big datasets (CASIA-B, CCPG, SUSTech1K, Gait3D, GREW). The results in the tables show it is better than many state-of-the-art methods, especially in difficult situations like changing clothes (CCPG dataset). The ablation studies are also good to show why their idea works.\n\nClarity is good. The paper is written well. It is easy to understand the main idea. Figure 3 and Figure 4 are very helpful to see how the model works and what it is learning.\n\nWeaknesses:\n\nThe \n\nVocabulary Base is very small, only eight words for the gait cycle.  The paper does not say if they tried more words or different kinds of words. This feels a bit limited.\n\nThe paper does not talk about the bad side of this technology. Gait recognition can be used for surveillance and tracking people without them knowing. This is a big ethical problem and the authors should have discussed it. In the checklist, they just said Not Applicable for societal impact, which is not right.\nThe model architecture for α-Gait is quite simple. It uses standard blocks like attention. The main new thing is the idea (Gait-World), not a new complex model design.", "was_revised": true, "final_justification": "After author feedback, i would keep my rating of accept.", "rebuttal": "Dear Reviewer VrEo:\n\nWe greatly appreciate your careful assessment and constructive insights. We provide detailed point‑by‑point responses below. Please let us know if any additional clarification is needed.\n\n---\n\n> [W1] The Vocabulary Base is very small, only eight words for the gait cycle. The paper does not say if they tried more words or different kinds of words. This feels a bit limited.\n\n[A] Thanks for your valuable comments. We further explain this as follows:\n\n- ***(i)*** The reason for vocabulary selection. The eight phases we adopt correspond to the canonical breakdown of a normal walking cycle established in clinical gait analysis [1, 2] where the statements (“It now is evident that each stride contains 8 functional patterns (phases) [1].” and “In order to describe the elements of gait, the cycle can be broken down further into 8 sub-factors [2].”) demonstrate that eight gait cycle terms potentially capture the spectrum of lower‑limb kinematics and kinetics and is therefore widely accepted as the minimal yet complete description of human walking.\n\n    [1] Burnfield M. Gait analysis: normal and pathological function[J]. Journal of Sports Science and Medicine, 2010.\n\n    [2] Whittle M W. Gait analysis: an introduction[M]. Butterworth-Heinemann, 2014.\n\n- ***(ii)*** More kinds of words. The gait network needs to extract representations that are invariant to complex covariates (e.g., cross-view, cross-bag and cross-clothing conditions). Therefore, we try to explore the vocabularies that describe common covariates: “view-angle invariant”, “bag invariant”, “clothing invariant” and evaluate the impact as follows: \n\n    | Model | Vocabulary Base | Rank-1 (%) | mAP (%) |\n    |------|------|------|------|\n    |Baseline | -   | 74.4   |65.8 |\n    | $\\alpha$-Gait   | [Initial contact, ..., loading response]   | 76.3   |67.8|\n    | $\\alpha$-Gait   | [Initial contact, ..., loading response, view-angle invariant, bag invariant, clothing invariant]   | 76.8   |68.4|\n\n    As the table shows, we further explore how gait-related terms can potentially affect recognition performance and refine our understanding of gaits. With the flexibility and extensibility of Gait-World, we will continue to explore the vocabularies in future work, thereby deepening understanding within the gait domain.\n\n- ***(iii)*** The extensibility for Gait-World. We use universal eight gait cycle terms to highlight the effectiveness of vocabularies. However, Gait-World serves as an intuitive and efficient way for researchers to refine their understanding of gait patterns, not limited to these words. \n\n---\n\n> [W2] The paper does not talk about the bad side of this technology. Gait recognition can be used for surveillance and tracking people without them knowing. This is a big ethical problem and the authors should have discussed it. In the checklist, they just said Not Applicable for societal impact, which is not right.\n\n[A] Thank you for raising this crucial issue. Safeguarding privacy remains our top priority as we advance AI research. This work discusses the ethical problem from three perspectives:\n\n- ***(i)*** This work exclusively studies publicly available datasets whose participants granted prior consent for academic use, and only silhouette data are used, suppressing conspicuous visual cues (e.g., faces).\n\n- ***(ii)*** We agree that gait recognition, like many biometric technologies, carries a real risk of misuse for covert surveillance and mass tracking. We firmly believe that gait recognition technology should be deployed only with explicit, informed consent or under a clearly defined legal mandate, and never for indiscriminate, covert mass surveillance.\n\n- ***(iii)*** We will add a dedicated ethics section and update the checklist accordingly.\n\n---\n\n> [W3] The model architecture for α-Gait is quite simple. It uses standard blocks like attention. The main new thing is the idea (Gait-World), not a new complex model design.\n\n[A] Thank you for the thoughtful analysis. We reorganize and highlight the main idea as follows:\n\n- ***(i)*** Gait-World provides a better human understanding of gaits and a new paradigm to complement existing paradigms. It serves as an intuitive and efficient tool for researchers to refine their understanding of gait patterns, providing new directions for gait research.\n\n- ***(ii)*** α-Gait designs Vocabulary Relation Mapper (VRM) to align vocabulary features into the gait space and establish associations among vocabularies, addressing feature distribution discrepancy between the Text Encoder and Gait Encoder. Furthermore, α-Gait designs the Gait Fine-grained Detector (GFD) to present an object detection perspective where the vocabulary feature detects gait features with the corresponding semantics, which demonstrates the effectiveness of vocabularies. Specifically, the main differences between α-Gait and the attention mechanism are: 1) The objectives. α-Gait aims to provide an intuitive demonstration of the vocabulary’s effectiveness for gait recognition. 2) The implementation. The Query is the text embeddings for guiding the gait network towards universal representations beyond the gait datasets.\n\nIn the main manuscript, we will reorganize the contribution to highlight the principal innovation.\n\n---\n\n> [Q1] Your Vocabulary Base has only eight phrases, which are the technical terms for a gait cycle. Why did you choose only these? Did you try using more simple or descriptive words like \"walking fast\", \"limping\", or \"carrying a bag\"?\n\n[A] Thank you for the thoughtful comments. Similar to the previous weakness, we respectfully invite you to see [W1].\n\n---\n\n> [Q2] α-Gait model uses a Text Encoder that is a big model. How much does this slow down the recognition time compared to a normal model like GaitBase? Can you give some numbers on inference speed?\n\n[A] Thank you for raising this point. Since the vocabulary is fixed and the Text Encoder is frozen, α-Gait can pre‑compute and cache the word embeddings to largely reduce the computation. The relative statistics are as follows:\n\n| Model | Venue | Rank-1 (%) | mAP (%) | Params. (M) | FLOPs. (G) |\n|------|------|------|------|------|------|\n|GaitBase| CVPR23   | 64.6  |- |19.3 |35.4|\n|GaitGL| ICCV23| -  |- |60.3 |58.6 |\n|DyGait| ICCV23| 66.3  |56.4 |133.1 |239.0|\n|HSTL| ICCV23| 61.3  |55.5 |54.7 |29.7|\n|DeepGaitV2| TPAMI25| 74.4  |65.8 |25.5 |85.3|\n| $\\alpha$-Gait| -   | 76.3  |67.8 |59.1 |85.6|\n\nThe table shows that α-Gait enhances performance without substantially increasing computational cost.\n\n---\n\n> [Q3] Your results on the CCPG dataset are much better than other methods, but on CASIA-B, the improvement is not so big. Can you explain why your method works so well for clothes-changing but is only competitive in normal view-changing scenarios?\n\n[A] Thank you for highlighting this point.  We observe two complementary causes:\n\n- ***(i)*** Appearance‑invariant vocabulary. The eight gait‑cycle words describe walking phases (e.g., heel‑strike, toe‑off, etc.), which are intrinsically independent of the appearance. They therefore retain identity cues when apparel changes drastically, giving a larger relative gain on clothes‑changing benchmarks.\n\n- ***(ii)*** Saturated CASIA-B recognition performance. CASIA‑B changes only upper‑body clothes. Therefore, the legs remain visually consistent in CASIA‑B and appearance‑based methods already extract stable motion cues, saturating the recognition performance (e.g., 98% on Normal Walking). In contrast, CCPG includes full‑body, partial and backpack variations, which severely confound appearance‑based methods, while our walk‑centric embeddings stay robust and thus yield a conspicuous margin.\n\n---\n\n> [Q4] Gait recognition is a biometric technology and can be used for surveillance. This has a negative societal impact. Can you please add a discussion about these risks and how they can be managed?\n\n[A] Thank you for highlighting this important issue. In addition to the covert mass surveillance and informed consent concerns noted in [W2], we further discuss that gait recognition, as a remote biometric, could be misused for algorithmic bias and function creep. Below, we outline the primary risks and the concrete safeguards:\n\n- ***(i)*** Risk 1: Covert mass surveillance \n\n    Technical safeguards: Release only binary silhouettes / skeletons (no RGB frames) and an explicit ban on live‑CCTV deployment without informed consent.\n\n- ***(ii)*** Risk 2: Informed consent \n\n    Technical safeguards: The data are released strictly for non‑commercial research, subject to case‑by‑case approval. If an algorithm nevertheless recovers a subject’s identity, that identity is deleted immediately and permanently. License requires erasure of recovered identities and mandatory reporting within 48 hours of any breach.\n\n- ***(iii)*** Risk 3: Algorithmic bias \n\n    Technical safeguards: Provide sub‑split test metrics (gender, age, assistive devices); if inequality‑deviation rate exceeds a threshold, retrain with re‑weighting and fairness regularization. Yearly bias reports in the model card for community review.\n\n- ***(iv)*** Risk 4: Function creep \n\n    Technical safeguards: Data and models released only after ethics board approval. Access will be immediately revoked, and any violations will be publicly disclosed if the technology is deployed beyond the approved scope.\n\n---\n\n> [L1] The authors have a small section on limitations, but it is only about future technical work, like using more words or making it a multimodal system. They do not at all discuss the main limitation which is the potential negative societal impact of gait recognition technology (e.g., surveillance). This is a serious miss.\n\n[A] Thank you for pointing out this gap. Similar to the previous weakness and question, we respectfully invite you to see [W2] and [Q4]. We will expand the Limitations & Societal Impact to address the ethical risks of gait recognition."}, {"review_id": "EnWCK8t0Uw", "reviewer": "Reviewer_xBEF", "summary": "The paper introduces a novel paradigm called Gait-World, which aims to improve gait recognition by integrating human-defined vocabulary with Vision-Language Models (VLMs). Existing appearance-based and model-based gait recognition methods suffer from limitations in interpretability and generalization. This work proposes to bridge that gap by leveraging semantic vocabulary related to human gait to guide gait feature learning by VLMs. The proposed method was evaluated on CASIA-B, CCPG, SUSTech1K, Gait3D and GREW.", "questions": "(1) Gait recognition requires fine-grained details, why the general phases text can help the gait recognition?\n(2) Gait of different people have same routine (initial contact, loading responses, ...), why the text can lead to better gait recognition performance?\n(3) Is it feasible to extend the gait vocabulary to further boost the performance?\n(4) Can the model take text related to appearance such as clothing, carrying, etc.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n(1) This paper introduces a new framework that uses human-understandable vocabulary (gait cycle phases) to guide gait representation learning.\n(2) By leveraging a text encoder, the semantic information may help the gait encoder learn better features. \n(3) Alpha-gait achieves strong results on benchmark datasets\n(4) The paper includes adequate ablation studies of the effect of vocabulary guidance, VLM type, and network components.\n\nWeaknesses:\n(1) The predefined vocabulary base only contain eight terms, which may not capture various gait patterns.\n(2) Despite framing the work as vocabulary-guided, all inputs share the same fixed set of textual prompts.\n(3) The encoder such as CLIP was not pretrained on gait-related data such as eilhouette, which may not align well with the words in the vocabulary.\n(4) There is no verification of semantic alignment of gait features and texts", "was_revised": true, "final_justification": "The rebuttal addressed most of my concerns. I keep my original rating.", "rebuttal": "Dear Reviewer xBEF:\n\nThank you for your insightful feedback and thoughtful suggestions. Below we respond point‑by‑point, clarifying our methods and results where needed. Please feel free to let us know if any issues remain.\n\n---\n\n> [W1] The predefined vocabulary base only contains eight terms, which may not capture various gait patterns.\n\n[A] Thank you for the thoughtful comment. We address this concern from three perspectives:\n\n- ***(i)*** The reason for vocabulary selection. The statements in clinical gait literatures [1, 2] (“It now is evident that each stride contains 8 functional patterns (phases) [1].” and “In order to describe the elements of gait, the cycle can be broken down further into 8 sub-factors [2].”) consistently partitions a normal stride into eight distinct phases. Hence, using eight gait‑cycle terms is widely accepted as the minimal yet comprehensive way to describe lower‑limb kinematics and kinetics during walking.\n\n    [1] Burnfield M. Gait analysis: normal and pathological function[J]. Journal of Sports Science and Medicine, 2010.\n\n    [2] Whittle M W. Gait analysis: an introduction[M]. Butterworth-Heinemann, 2014.\n\n- ***(ii)*** More kinds of words. Since gait recognition needs to stay robust to common covariates (e.g., cross‑view, cross‑bag and cross‑clothing conditions), we additionally experimented with more vocabularies: “view-angle invariant”, “bag invariant”, “clothing invariant”, and we analyze the effect as reported:\n\n    | Model | Vocabulary Base | Rank-1 (%) | mAP (%) |\n    |------|------|------|------|\n    |Baseline | -   | 74.4   |65.8 |\n    | $\\alpha$-Gait   | [Initial contact, ..., loading response]   | 76.3   |67.8|\n    | $\\alpha$-Gait   | [Initial contact, ..., loading response, view-angle invariant, bag invariant, clothing invariant]   | 76.8   |68.4|\n\n    As the table shows, we further explore how gait-related terms can potentially affect recognition performance and refine our understanding of gaits. With the flexibility and extensibility of Gait-World, we will continue to explore the vocabularies in future work, thereby deepening understanding within the gait domain.\n\n- ***(iii)*** The extraction of gait patterns. This work treats these words as semantic queries to compare different gait sequences. Therefore, the model will learn fine‑grained gait patterns adaptively from the gait data, preserving the diversity of gait patterns.\n\n---\n\n> [W2] Despite framing the work as vocabulary-guided, all inputs share the same fixed set of textual prompts.\n\n[A] Thank you for highlighting this issue. We intentionally keep the vocabulary fixed for all samples:\n\n- ***(i)*** Unified semantics for fine-grained representations. A shared vocabulary base anchors every sequence to the same semantic directions, allowing the visual encoder to capture the precise and fine‑grained identity cues. \n\n- ***(ii)*** Text Guidance against appearance noises. Gait recognition needs to capture identity from the complex environment, such as view angles and clothes. A consistent textural prompt acts as an external prior, guiding the network toward identity‑specific information rather than appearance‑based confounders.\n\n---\n\n> [W3] The encoder such as CLIP was not pretrained on gait-related data such as silhouette, which may not align well with the words in the vocabulary.\n\n[A] Thank you for raising this issue. We address the concern from two perspectives:\n\n- ***(i)*** The modal alignment is learnable. The Vocabulary Relation Mapper (VRM) and Gait Fine-grained Detector (GFD) are jointly optimized under an identity‑supervised loss, which forces the silhouette features to align with the eight textual directions, so recognition does not only depend on the text encoder.\n\n- ***(ii)*** The text space within VLMs exhibits strong universal capability. Although CLIP’s pre‑training set lacks silhouettes and Llama is a text‑only framework, their text embeddings still can construct general human‑motion concepts (e.g., “initial swing,” “loading response”). During training, the visual encoder learns the silhouette patterns with these universal axes, giving the network covariate‑invariant features that extend beyond the gait training set.\n\n---\n\n> [W4] There is no verification of semantic alignment of gait features and texts?\n\n[A] Thanks for your valuable comments. We visualize the process of vocabulary guidance in Figure 4 of the main manuscript, where GFD detects the eight silhouettes with the corresponding eight gait cycle vocabularies. Additionally, we further conduct more visualizations that consistently corroborate the alignment between gait features and their textual counterparts. Owing to the latest NeurIPS rebuttal constraints (No permission for the additional images or links), we will present more results in the revised manuscript.\n\n---\n\n> [Q1] Gait recognition requires fine-grained details, why the general phases text can help the gait recognition?\n\n[A] Thanks for this valuable question. Similar to the previous weaknesses, we respectfully invite you to see [W1, W2].\n\n---\n\n> [Q2] Gait of different people have same routine (initial contact, loading responses, ...), why the text can lead to better gait recognition performance?\n\n[A] Thanks for raising this important point. Similar to the previous weaknesses, we respectfully invite you to see [W1, W2].\n\n---\n\n> [Q3] Is it feasible to extend the gait vocabulary to further boost the performance?\n\n[A] Thanks for this suggestion. We explore vocabulary expansion on top of the eight gait cycle terms and observe consistent improvements. Similar to the previous weakness, we respectfully invite you to see [W1].\n\n---\n\n> [Q4] Can the model take text related to appearance such as clothing, carrying, etc.\n\n[A] Thanks for the valuable comment. We add appearance vocabularies: “view-angle invariant”, “bag invariant”, “clothing invariant”. Similar to the previous weakness, we respectfully invite you to see [W1]."}, {"review_id": "VGtIkvME5N", "reviewer": "Reviewer_yWT3", "summary": "This paper introduces vocabulary to the gait recognition, which is a probably new recognition paradigm besides conventional appearance and model-based approaches. Specifically, a cross-attention based architecture, which is referred as α-Gait, is proposed for guiding gait feature learning with vocabulary cues.", "questions": "1. α-Gait integrates both components into a unified module. Treating them as separate and simple components diminishes the overall novelty of the method.\n2. Many but redundant datasets. Authors can remove some simple datasets such as CASIA-B [1] and add more modality-generalizability experiments.\n\n[1] Shiqi Yu, Daoliang Tan, and Tieniu Tan. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In 18th International Conference on Pattern Recognition (ICPR’06), volume 4, pages 441–444. IEEE, 2006.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The motivation is clear. Leveraging certain vocabulary bases to explore the potential integration of LLM in gait analysis is worthwhile.\n2. The design of vocabulary base is reasonable with respect to the gait cycle.\n\nWeaknesses:\n1. The proposed α-Gait lacks of novelty. Although effective, its main components, including Vocabulary Relation Mapper and Gait Fine-grained Detector, are actually existing cross-attention architectures.\n2. Vocabulary base lacks of modality-generalizability. The paper repeatedly claims that a-gait can complement existing paradigms such as in lines 237. However, the experiments only present results based on pedestrian silhouettes. The authors are encouraged to validate the generalizability of α-Gait by applying it to other modality like skeleton-maps or point clouds.\n3. Computational resource cost. 1) α-Gait introduces additional resource cost. 2) In Table 5, the block numbers of α-Gait are [2, 4, 4, 2] with channels [64, 128, 256, 512], which is larger than most compared methods. Therefore, comparisons of parameters size and FLOPs are recommended.\n4. It is better to make code publicly available.", "was_revised": true, "final_justification": "The authors' feedbacks have addressed my concerns on experiments on more challenging datasets. But I feel the novelty is somewhat limited. I therefore keep my initial rating.", "rebuttal": "Dear Reviewer yWT3:\n\nWe greatly value the time and expertise you invested in evaluating our work. Your detailed observations have highlighted important points we can refine, and we address each of them below. Should anything remain unclear, we welcome further discussion.\n\n---\n\n> [W1] The proposed α-Gait lacks of novelty. Although effective, its main components, including Vocabulary Relation Mapper and Gait Fine-grained Detector, are actually existing cross-attention architectures.\n\n[A] Thanks for your valuable comments. The cross-attention is the partial implementation detail and not the primary contribution. We further clarify the main idea: \n\n- ***(i)*** Gait-World provides a better human understanding of gaits, and a new paradigm to complement existing paradigms (i.e., appearance-based and model-based methods). It serves as an intuitive and efficient tool for researchers to refine their understanding of gait patterns, providing new directions for gait research.\n\n- ***(ii)*** α-Gait designs Vocabulary Relation Mapper (VRM) to align vocabulary features into the gait space and establish associations among vocabularies, addressing feature distribution discrepancy between the Text Encoder and Gait Encoder. Furthermore, α-Gait designs Gait Fine-grained Detector (GFD) to present an object detection perspective where the vocabulary feature detects gait features with the corresponding semantics, which demonstrates the effectiveness of vocabularies. Specifically, the main differences between α-Gait and the attention mechanism are: 1) The objectives. α-Gait aims to provide an intuitive demonstration of the vocabulary’s effectiveness for gait recognition. 2) The implementation. The Query is the text embeddings for guiding the gait network towards universal representations beyond the gait datasets.\n\nIn the main manuscript, we will reorganize the contribution to highlight the principal innovation.\n\n---\n\n> [W2] Vocabulary base lacks of modality-generalizability. The paper repeatedly claims that a-gait can complement existing paradigms such as in lines 237. However, the experiments only present results based on pedestrian silhouettes. The authors are encouraged to validate the generalizability of α-Gait by applying it to other modality like skeleton-maps or point clouds.\n\n[A] Thanks for highlighting the suggestions. We extend α‑Gait from silhouettes to skeleton sequences, and the results confirm that the vocabulary guidance remains effective beyond a single modality. Specifically, we conduct the experiment with SkeletonGait[1] in α-Gait framework on Gait3D：\n\n| Model | Venue | Rank-1 (%) | mAP (%) |\n|------|------|------|------|\n|SkeletonGait | AAAI24  | 37.9   |29.6 |\n| SkeletonGait with α-Gait   | -  | 40.3   |31.3|\n|SkeletonGait++ | AAAI24  | 76.0   |69.2 |\n| SkeletonGait++ with α-Gait   | -  | 78.1   |71.5|\n\nThe table shows the reproduction performance where the vocabulary can also be effective in other modalities (i.e., Skeleton maps), which further proves the modality-generalizability.\n\n[1] Fan C, Ma J, Jin D, et al. Skeletongait: Gait recognition using skeleton maps[C]//Proceedings of the AAAI conference on artificial intelligence. 2024.\n\n---\n\n> [W3] Computational resource cost. 1) α-Gait introduces additional resource cost. 2) In Table 5, the block numbers of α-Gait are [2, 4, 4, 2] with channels [64, 128, 256, 512], which is larger than most compared methods. Therefore, comparisons of parameters size and FLOPs are recommended.\n\n[A] Thanks for this comment. The classification head (e.g., linear layers) scales linearly with the number of identities, overwhelmingly dominating the parameter count in most gait-recognition. Specifically, GREW contains roughly 20k IDs. To highlight differences in the computation cost more clearly, we therefore report comparison results on Gait3D (e.g., smaller IDs about 3k), including α-Gait with [2, 4, 4, 2] and [1, 4, 4, 1] backbones:\n\n| Model | Venue | Rank-1 (%) | mAP (%) | Params. (M) | FLOPs. (G) |\n|------|------|------|------|------|------|\n|GaitBase| CVPR23   | 64.6  |- |19.3 |35.4|\n|GaitGL| ICCV23| -  |- |60.3 |58.6 |\n|DyGait| ICCV23| 66.3  |56.4 |133.1 |239.0|\n|HSTL| ICCV23| 61.3  |55.5 |54.7 |29.7|\n|DeepGaitV2| TPAMI25| 74.4  |65.8 |25.5 |85.3|\n| α-Gait with [2, 4, 4, 2] | -   | -  |- |64.7 |120.9|\n| α-Gait with [1, 4, 4, 1] | -   | 76.3  |67.8 |59.1 |85.6|\n\nThe table shows that α-Gait enhances performance without substantially increasing computational cost.\n\n---\n\n> [W4] It is better to make code publicly available.\n\n[A] Thanks for the suggestion. We will consolidate the implementation and open‑source the complete codebase.\n\n---\n\n> [Q1] α-Gait integrates both components into a unified module. Treating them as separate and simple components diminishes the overall novelty of the method.\n\n[A] Thank you for the valuable feedback. Similar to the previous weaknesses, we respectfully invite you to see [W1].\n\n---\n\n> [Q2] Many but redundant datasets. Authors can remove some simple datasets such as CASIA-B and add more modality-generalizability experiments.\n\n[A] Thank you for the constructive suggestion. We will revise our experimental setup and dedicate additional studies to modality generalizability. For example, incorporating skeleton maps experiments as suggested in [W2]."}, {"review_id": "6sK1E9zcYz", "reviewer": "Reviewer_dYxT", "summary": "This paper proposes Gait-World, a new paradigm for gait recognition that uses human-interpretable vocabulary and Vision-Language Models to guide feature learning. And proposed α-Gait, which bridges text and gait modalities via a Vocabulary Relation Mapper and Fine-grained Detector. Experiments on multiple datasets show improved robustness to clothing changes and occlusions, demonstrating how semantic vocabulary can enhance gait recognition while maintaining efficiency.", "questions": "(1) In the problem description on lines 34-36, I think \"Ambiguous gait concepts\" would be a very appealing point, but the author didn't explain it clearly.  The same goes for the analysis in the abstract.\n\n(2) The paper claims that VLMs are insensitive to gait silhouettes, but only the detection results of GPT are shown in Figure 2. Can the detection results of other VLMs be provided to support this view? And can all VLMs identify the gait cycle from the silhouettes?\n\n(3) The predefined gait cycle actions appear biomechanically inspired. What empirical evidence supports that these 8 phases are optimal? Could a data-driven vocabulary discovery approach (e.g., clustering VLM embeddings) yield more discriminative terms? How does performance vary with vocabulary size/granularity?\n\n(4) In line 195,  the author points out that \"Horizontal Partitioning preserves temporal dimensions\", but VRM/GFD processes each frame independently.", "limitations": "Yes.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n(1) The illustrations in this paper are beautiful and can convey the meaning of the text.\n\n(2) The motivation of this paper is significant. It is quite convincing to utilize the universal features of VLMs to assist gait recognition.\n\n(3) The writing of this article is easy to understand, and the language is fluent.\n\n(4) The experimental results were extremely impressive.\n\nWeaknesses:  Please refer to Limitations.", "was_revised": true, "final_justification": "Thank you for your rebuttal. You've resolved most of my doubts. Thus I will maintain my rating.", "rebuttal": "Dear Reviewer dYxT:\n\nThank you for the constructive critique and encouraging remarks. We respond to each concern in the following sections, and we welcome any follow-up questions you may have.\n\n---\n\n> [W1] Please refer to Limitations.\n\n[A] Thank you for your valuable comment. The manuscript mainly provides limitations as follows:\n\n- ***(i)*** Limitation 1: Expanding the vocabulary. We try to explore the vocabularies that describe common covariates: “view-angle invariant”, “bag invariant”, “clothing invariant” and evaluate the impact as follows:\n\n    | Model | Vocabulary Base | Rank-1 (%) | mAP (%) |\n    |------|------|------|------|\n    |Baseline | -   | 74.4   |65.8 |\n    | $\\alpha$-Gait   | [Initial contact, ..., loading response]   | 76.3   |67.8|\n    | $\\alpha$-Gait   | [Initial contact, ..., loading response, view-angle invariant, bag invariant, clothing invariant]   | 76.8   |68.4|\n\n    As the table shows, we further explore how gait-related terms can potentially affect recognition performance and refine our understanding of gaits. With the flexibility and extensibility of Gait-World, we will continue to explore the vocabularies in future work, thereby deepening understanding within the gait domain.\n\n- ***(ii)*** Limitation 2: Extending the method to a multimodal paradigm. We extend α‑Gait from silhouettes to skeleton sequences, and the results confirm that the vocabulary guidance remains effective beyond a single modality. Specifically, we conduct the experiment with SkeletonGait[1] in α-Gait framework on Gait3D：\n\n    | Model | Venue | Rank-1 (%) | mAP (%) |\n    |------|------|------|------|\n    |SkeletonGait | AAAI24  | 37.9   |29.6 |\n    | SkeletonGait with α-Gait   | -  | 40.3   |31.3|\n    |SkeletonGait++ | AAAI24  | 76.0   |69.2 |\n    | SkeletonGait++ with α-Gait   | -  | 78.1   |71.5|\n\n    The table shows the reproduction performance where the vocabulary can also be effective in other modalities (i.e., Skeleton maps), which further proves the modality-generalizability.\n\n    [1] Fan C, Ma J, Jin D, et al. Skeletongait: Gait recognition using skeleton maps[C]//Proceedings of the AAAI conference on artificial intelligence. 2024.\n\n---\n\n> [Q1] In the problem description on lines 34-36, I think \"Ambiguous gait concepts\" would be a very appealing point, but the author didn't explain it clearly. The same goes for the analysis in the abstract.\n\n[A] Thanks for this valuable suggestion. “Ambiguous gait concepts” indicate the broad gait terms, such as spatial, temporal, or structural information, that many studies use to characterize gait features without specifying exactly what they capture. Clarifying these concepts is crucial for two reasons:  \n\n- ***(i)*** Slow Gait development. Vague terminology slows progress in the rapidly evolving gait recognition field. For example, research may only stop at designing better backbones instead of probing deeper into gait semantics.  \n\n- ***(ii)*** Difficult practice verification. When new and precise gait concepts emerge, researchers often lack a straightforward way to implement and validate them. \n\nGait-World serves as an intuitive and efficient tool for researchers to refine their understanding of gait patterns, and α-Gait further provides a demonstration of the vocabulary’s effectiveness.\n\n---\n\n> [Q2] The paper claims that VLMs are insensitive to gait silhouettes, but only the detection results of GPT are shown in Figure 2. Can the detection results of other VLMs be provided to support this view? And can all VLMs identify the gait cycle from the silhouettes?\n\n[A] Thanks for raising this point. Owing to the latest NeurIPS rebuttal constraints (No permission for the additional images or links), we will present more results in the revised manuscript. We present the same silhouette and question (Figure 2 in the main manuscript) to three top-tier VLMs: GPT‑o3, Qwen3‑235B, and Kimi‑K2, which obtain highly consistent outcomes:\n\n- ***(i)*** GPT‑o3. Answer: (E) pre‑swing, Reason: The rear foot shows heel‑off with only the toes likely still touching, and the limb has started knee flexion and unloading—classic signs that it is just about to leave the ground to enter initial swing.\n\n- ***(ii)*** Qwen3‑235B: Answer: E, Reason: The stance leg (right) remains flat on the ground, and the swing leg (left) appears to initiate lifting with minimal knee flexion, aligning with pre-swing characteristics where the foot is still in contact.\n\n- ***(iii)*** Kimi‑K2: Answer: E, Reason: The trailing limb is in late stance with the heel off and only the forefoot pushing, indicating the transition to toe-off that defines pre-swing.\n\nAs the above analysis shows, mainstream VLMs can identify gait cycles from silhouettes based on the overall characteristics, which motivates us to leverage textual guidance to learn more universal gait representations.\n\n---\n\n> [Q3] The predefined gait cycle actions appear biomechanically inspired. What empirical evidence supports that these 8 phases are optimal? Could a data-driven vocabulary discovery approach (e.g., clustering VLM embeddings) yield more discriminative terms? How does performance vary with vocabulary size/granularity?\n\n[A] Thank you for raising these points. The eight phases we adopt correspond to the canonical breakdown of a normal walking cycle established in clinical gait analysis [1, 2] where the statements (“It now is evident that each stride contains 8 functional patterns (phases) [1].” and “In order to describe the elements of gait, the cycle can be broken down further into 8 sub-factors [2].”) demonstrate that eight gait cycle terms potentially capture the spectrum of lower‑limb kinematics and kinetics and is therefore widely accepted as the minimal yet complete description of human walking.\n\n[1] Burnfield M. Gait analysis: normal and pathological function[J]. Journal of Sports Science and Medicine, 2010.\n\n[2] Whittle M W. Gait analysis: an introduction[M]. Butterworth-Heinemann, 2014.\n\nIt is worth noting that the eight words are merely a universal starting point, not an upper bound. We provide an alternative way to explore vocabulary size and granularity where we add words that describe common covariates, view angle invariant, bag invariant, and clothing invariant. The results are as follows:\n\n| Model | Vocabulary Base | Rank-1 (%) | mAP (%) |\n|------|------|------|------|\n|Baseline | -   | 74.4   |65.8 |\n| $\\alpha$-Gait   | [Initial contact, ..., loading response]   | 76.3   |67.8|\n| $\\alpha$-Gait   | [Initial contact, ..., loading response, view-angle invariant, bag invariant, clothing invariant]   | 76.8   |68.4|\n\nAs the table shows, we further explore how gait-related terms can potentially affect recognition performance and refine our understanding of gaits. With the flexibility and extensibility of Gait-World, we will continue to explore the vocabularies in future work, thereby deepening understanding within the gait domain.\n\n---\n\n> [Q4] In line 195, the author points out that \"Horizontal Partitioning preserves temporal dimensions\", but VRM/GFD processes each frame independently.\n\n[A] Thanks for indicating this question. The statement just highlights that VRM/GFD are applied after Horizontal Partitioning (HP) and process each frame independently. We further clarify this: The gait framework generally contains basic modules: Temporal Pooling (TP) followed by HP. In this work, we only apply HP, which allows the temporal dimension to be preserved before VRM and GFD. We will revise the manuscript to make this clearer."}]

Author rebuttal:

Meta-review: This paper introduces Gait-World and the α-Gait model, a novel paradigm that leverages human-interpretable vocabulary with vision-language models to guide gait recognition. The motivation is clear, and the approach offers a fresh perspective that bridges language and vision for biometric analysis. Experiments across five benchmark datasets demonstrate strong performance, particularly under challenging conditions such as clothing changes, with solid ablations validating the design choices.

Reviewers appreciated the originality, clarity, and empirical strength of the work, though some raised concerns about the limited vocabulary base, potential societal impacts of gait recognition, and the relatively simple model design. The rebuttal addressed these points by providing further justification of the vocabulary selection, evidence of extensibility to other words and modalities, efficiency analysis, and an explicit discussion of ethical risks and safeguards. Most reviewers were satisfied with the responses and several leaned more positively after rebuttal.

Overall, the paper presents a technically solid and original contribution with significant potential impact. I recommend acceptance.
Decision: Accept (poster)