3D Skeleton-Based Action Recognition: A Review

Bin Ren; Hong Liu; Jiajun Wen; Jiaying Lin; Junsong Yuan; Mengyuan Liu; Qianshuo Hu

REVIEW 5 major objections 5 minor 278 references

3D Skeleton-Based Action Recognition: A Review

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read This review claims that 3D skeleton-based action recognition is best organized as a pipeline of task stages—modality derivation, augmentation, representation, feature extraction, and spatio-temporal modeling—rather than by model…

desk verdict A useful task-oriented organizing idea, but the manuscript's copied figure captions and structural errors make it untrustworthy as a review. read the letter →

arxiv 2506.00915 v1 pith:EIWS6YGD submitted 2025-06-01 cs.CV

Mengyuan Liu , Hong Liu , Qianshuo Hu , Bin Ren , Junsong Yuan , Jiaying Lin , Jiajun Wen This is my paper · ORCID

classification cs.CV

keywords actionrecognition3Dskeletontask-orientedreviewdataaugmentationrepresentationspatio-temporalmodelingstate-of-the-artbenchmarksMambaandLLMmethods

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

This review's central claim is that 3D skeleton-based action recognition has been surveyed the wrong way around: organizing the literature by model architecture—RNNs, CNNs, GCNs, Transformers—hides the task's essential structure. The authors propose a task-oriented framework that decomposes the field into pipeline stages—derived modalities, data augmentation, data representation, feature extraction, and spatio-temporal modeling—and argue that preprocessing steps deserve the same attention as network design. If the framework is right, it gives researchers a more intrinsic roadmap: each stage can be studied, reported, and optimized independently, and emerging approaches such as hybrid networks, Mamba, large language models, and generative models fit naturally into the same stages. The review also gathers public datasets and state-of-the-art benchmark numbers as evidence for how the staged pipeline plays out in practice. A sympathetic reader would take it as the claim that how you organize a survey shapes what questions the field asks, and that the pipeline-stage organization is the more fundamental one.

What carries the argument

The organizing machinery is the pipeline-stage framework itself (Figure 1 of the paper), formalized with a small set of equations. Raw input is a skeleton sequence $S = \{X_t \mid t=1,\dots,T\}$ with $X_t \in \mathbb{R}^{J\times D}$; the derived modalities are defined by coordinate differences: $X^{\text{bone}}_t = X^{\text{joint1}}_t - X^{\text{joint2}}_t$, $X^{\text{jm}}_t = X^{\text{joint}}_t - X^{\text{joint}}_{t-1}$, and $X^{\text{bm}}_t = X^{\text{bone}}_t - X^{\text{bone}}_{t-1}$. The four representations map the sequence onto the input formats of the four network families, and spatio-temporal modeling is reduced to three structural forms—serial ($F = \Phi_{\text{temporal}}(\Phi_{\text{spatial}}(\mathcal{S}))$), parallel ($F = \Phi_{\text{temporal}}(\mathcal{S}) + \Phi_{\text{spatial}}(\mathcal{S})$), and fusion ($F = \text{Fusion}(\Phi_{\text{temporal}}(\mathcal{S}), \Phi_{\text{spatial}}(\mathcal{S}))$)—which together let every reviewed method be placed at a stage and in a structural slot. This taxonomy is what carries the argument that the field can be surveyed completely without classifying by backbone architecture.

What would settle it

Re-run the top entries of Tables 2 and 3 under one fixed protocol—the same NTU RGB+D 120 split, the same joint-and-bone input streams, and no ensemble averaging—and check whether the reported ranking and margins survive; any large drop in a table-topping figure would falsify the comparative state-of-the-art claims. The completeness of the taxonomy could also be falsified by exhibiting a published method whose spatio-temporal coupling fits none of the serial, parallel, or fusion structures.

Watch

Extended reading notes

Core claim

The paper's central claim is that a task-oriented decomposition is a more essential and more complete way to understand 3D skeleton-based action recognition than the architecture-based classification used by previous surveys. It organizes the field along the actual processing chain: deriving the four generalized skeleton modalities (Joint, Bone, Joint-Motion, Bone-Motion) from raw coordinates, augmenting skeleton sequences with normal, extreme, mixing, and viewpoint-invariant transformations, converting the unstructured data into sequential, pseudo-image, graph, or token representations, extracting spatial and temporal features, and finally co-modeling space and time through serial, parallel, or fusion structures. The paper holds that preprocessing and representation stages strongly influence final accuracy, so reviews that skip straight to modeling miss a large part of what determines performance. It further claims that recent advances—hybrid architectures, Mamba-based state-space models, LLM-assisted feature extraction, and generative pre-training—are best understood within the same staged workflow rather than as new architecture families. The benchmark tables and dataset summaries are presented as the evidence that the staged pipeline, applied across the field, is where the measured progress lives.

Load-bearing premise

The state-of-the-art tables assume that accuracy figures taken from different papers were produced under the same evaluation protocol, the same input modalities, and the same training or ensembling conditions, so that the numbers can be compared as if they came from one experiment.

Editorial extensions

If this is right

If the framework is right, reporting norms should change: papers would describe modality sets and augmentation schedules as first-class methodological choices on par with network depth, because both stages are claimed to shape accuracy.
Researchers new to the field get a staged roadmap; each stage can be optimized and evaluated in isolation before integration, which the review frames as the intrinsic structure of the task.
The framework absorbs the newest architectures—hybrid networks, Mamba state-space models, LLM prompting, and generative pre-training—as instances of assisted feature extraction or spatio-temporal co-modeling rather than as a new category of model.
The benchmark analysis identifies NTU RGB+D 120 as the most demanding current protocol, so progress claims in the field should be judged against it, not merely against NTU 60 or Kinetics numbers.
The completeness claim implies that any future method can be located in the framework; the survey thereby positions itself as the reference map for subsequent work.

Reading between the lines

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

The framework implicitly predicts a measurable claim the paper does not test: on a fixed benchmark and protocol, variation in modality design and augmentation strategy accounts for a comparable share of accuracy spread as backbone choice; a controlled study holding the network fixed while varying preprocessing could test it.
If the benchmark numbers in Tables 2 and 3 are not protocol-comparable, the review's SOTA rankings are fragile; an unstated corollary is that the field would benefit from a shared evaluation harness that standardizes splits, input streams, and ensemble policy.
The serial/parallel/fusion trichotomy resembles a general design pattern that could be exported beyond skeletons, for instance to other structured time-series recognition tasks, where the same three ways of coupling spatial and temporal modules recur.
The taxonomy suggests a natural organization for a future leaderboard or benchmark suite, with accuracy reported per stage configuration rather than per model name.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Dear colleague,

The headline: this survey has a sensible organizing idea, but the submitted text is not trustworthy as a review. The task-oriented pipeline (modalities, augmentation, representation, extraction, modeling) is a reasonable way to structure the literature, and the coverage of Mamba, LLM, and generative methods is timely. If cleaned up, it could serve as a decent entry point for newcomers.

What is actually new: the emphasis on preprocessing steps—modality derivation and augmentation—gets more space than in most prior surveys, and the four-modality schema (Joint/Bone/Joint-Motion/Bone-Motion) is neatly formalized in Section 2. The three broad spatio-temporal structures (serial/parallel/fusion) are a reasonable taxonomy. The SOTA tables are typical of the field.

The problems are serious. Several figure captions contain verbatim first-person text from the papers being reviewed: Figure 5 includes a full section from a contrastive learning paper, including 'We do NOT require feature engineering'; Figure 8 contains another paper's experimental section with 'our multi-stream CNN model'; Figure 9 carries the HD-GCN methodology. Figure 4 even retains an IEEE Xplore download watermark. This is not a minor editing slip. It means the authors' descriptions are not consistently their own, and a reader cannot rely on the roadmap's content. There is also a duplicated paragraph in Section 6.1 (the ST-GCN/temporal-graph passage is repeated verbatim), and the introduction promises a datasets section as Section 8, but the actual Section 8 is Future Work—the promised comprehensive dataset overview is not delivered in the place advertised.

The framework itself is not circular and does not depend on any derived claims; the authors cite their own prior survey [51] transparently, and the overlap is expected. But the textual integrity issues undermine the central claim of a comprehensive, reliable review. This needs a full cleanup: rewrite the captions, remove borrowed text, fix the duplication, restore the dataset section, and remove the watermark. Until then, the paper cannot function as a trustworthy reference.

Who this is for: a reader who wants a task-structured map of the field might find value after these fixes. As submitted, I wouldn't cite it.

Recommendation: desk reject in current form; if the authors resubmit a cleaned version, send it to peer review.

Best,

Referee Report

5 major / 5 minor

Summary. This manuscript is a survey of 3D skeleton-based action recognition. Its stated contribution is a task-oriented framework that organizes the field by pipeline stages—skeleton modalities, data augmentation, data representation, feature extraction, and spatio-temporal modeling—rather than by network architecture, and that additionally covers recent hybrid, Mamba, LLM, and generative approaches together with benchmark datasets and state-of-the-art accuracy tables. The central claim is that this organization provides a more intrinsic and complete roadmap than prior model-centric surveys. The paper contains no new derivations; its evidentiary basis is the completeness and accuracy of the literature coverage.

Significance. A well-executed survey with this task-oriented organization would be useful: it would complement architecture-centric surveys, highlight preprocessing and representation choices that are often underweighted, and bring recent LLM/Mamba/generative work into a unified discussion. The authors also deserve credit for attempting broad coverage across many methods and datasets and for including explicit equations for the four common skeleton modalities. However, the value of a survey depends on the reliability and original synthesis of its descriptions. As submitted, the manuscript contains substantial unattributed source text inside figure captions, a promised datasets section that is absent, a duplicated paragraph in Section 6.1, and SOTA tables without stated comparability conditions. These problems directly undercut the central claim of a trustworthy, comprehensive, task-oriented roadmap. For this reason, the current version does not meet the standard for publication.

major comments (5)

[Figure 5 caption (also Figures 6, 8, 9)] Several figure captions contain substantial verbatim text from the cited papers, including first-person methodology and experimental description. For example, the caption of Figure 5 reproduces an entire section titled "III. PROPOSED APPROACH" from a contrastive-learning paper, including the sentence "We do NOT require feature engineering like [5], [6], [28] or designing task-specific models..." and that paper's internal figure reference "Take Figure 5: Visualization of normal augmentations." Similarly, the caption of Figure 8 includes a full experimental section with "our multi-stream CNN model" and implementation details from the shape-motion paper, and the caption of Figure 9 includes the HD-GCN paper's methodology and equations. These are not the authors' own syntheses, and they are not presented as quotations. Because the review's central claim is that it provides a reliable, accessible structured roadmap, this pervasive source-text contamination undermines the trustworthiness of the review's descriptions and therefore its central claim.
[Section 1, Section 8] The Introduction states that "Section 8 lists commonly used datasets and the performance of state-of-the-art models on these datasets," and the abstract promises "a comprehensive overview of public 3D skeleton datasets." In the delivered manuscript, Section 8 is actually titled "Future Work," while the dataset descriptions and Tables 1–3 are placed at the end of Section 7, after the Mamba paragraph, without a section heading. A core component promised in the abstract and introduction is thus missing from its announced location, which contradicts the paper's own structure and weakens the claimed comprehensiveness.
[Section 6.1, "Temporal Feature Extraction Using Temporal Graphs"] The same content is presented twice in near-verbatim form. The paragraph beginning "Graph Convolutional Networks (GCNs) have been shown to excel in modeling skeleton space..." is followed, after a short intervening paragraph, by a second paragraph beginning "Graph Convolutional Networks (GCNs) are highly effective in modeling skeleton structures..." that repeats the ST-GCN, temporal graph router, and Shift-GCN descriptions. This duplication is a concrete editorial defect that further reduces confidence in the accuracy of the review's content.
[Tables 2 and 3] The state-of-the-art comparison is not supported by stated comparability conditions. Tables 2 and 3 report accuracy numbers from different papers without specifying the selection criteria for entries or verifying that the compared numbers were obtained under the same evaluation protocols, input streams, ensemble settings, and preprocessing choices. On NTU RGB+D and Kinetics benchmarks, such details are known to differ across papers and can change rankings. Without explicit caveats or a consistent protocol audit, the comparative conclusions drawn from these tables are not reliable. Since the abstract advertises "an analysis of state-of-the-art algorithms evaluated on these benchmarks," this is a load-bearing issue.
[Section 7 and Figure 11] The paper categorizes spatio-temporal modeling into serial, parallel, and fusion structures, but gives no operational rule for assigning a method to one of the three categories. Some methods described in Section 7.3, such as MS-G3D's "unified spatiotemporal graph convolution module" and STSF-GCN's SlowFast-style dual pathways, appear to blend or transcend these categories. If the taxonomy is intended to be a core part of the proposed task-oriented framework, the criteria for the trichotomy and its exhaustiveness need to be stated explicitly.

minor comments (5)

[Figure 4 caption] The caption contains an IEEE Xplore watermark line, "Authorized licensed use limited to: SUN YAT-SEN UNIVERSITY...", which should be removed.
[Table 2] The header "Other Msethods" should read "Other Methods," and the row "SLnL-rFA [227] 2019 36.6" appears twice.
[Table 3] In the CTR-GCN row, the NTU RGB+D 120 Cross-Setup entry is truncated as "90."; a digit or digits appear to be missing.
[Section 2, Eqs. (1)–(13)] Several mathematical symbols render as placeholder glyphs (for example, the script symbols in the definitions of the augmentation and representation functions), making the formalization difficult to read.
[Figure 7 (d)] The token-based representation panel contains the stray text "???6,7", which appears to be a leftover annotation from a source figure.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation chain; the survey's framework is a presentational taxonomy and its self-citations are not load-bearing.

full rationale

This paper is a literature review, not a method paper with a derivation chain. It does not fit parameters to data, compute predictions from fitted inputs, or invoke a uniqueness theorem. The central claim that a task-oriented pipeline organization (modalities, augmentation, representation, feature extraction, spatio-temporal modeling) is more fundamental than architecture-based classification is a taxonomic and expository choice, not a quantity derived from the paper's own equations. The formal definitions in Section 2 (e.g., bone, joint motion, serial/parallel/fusion structures) are standard reformulations of existing concepts and are not used to force any subsequent conclusion. The review cites the authors' own prior survey [51] and several of their own papers, but these citations are used for context and attribution, not as the justification for the review's organizational framework; no conclusion reduces to a self-citation. The evident quality problems (verbatim first-person text from other papers inside figure captions, a duplicated paragraph in Section 6.1, and the mismatch between the promised dataset section and the delivered Future Work section) undermine the review's reliability and completeness, but they are correctness issues rather than circularity. Because there is one instance of transparent self-citation that is not load-bearing, a minimal score above zero is assigned; there is no circular reasoning of the kind the axis targets.

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

A review introduces no fitted free parameters and no invented entities. The benchmark tables transcribe numbers from cited papers, treated as external inputs rather than fitted values. Terms such as viewpoint-invariant transforms and Skeleton AdaIN are descriptions of cited prior work, not new postulates of this paper. The assumptions that matter are the trustworthiness of transcribed benchmark numbers and the adequacy of the review's own taxonomies.

assumptions (3)

domain assumption The accuracy figures in Tables 2 and 3 are accurate transcriptions of the cited papers and are directly comparable across methods.
The SOTA analysis relies on trusting reported benchmark numbers and their comparability; no reproduction, protocol adjustment, or selection criteria are provided.
ad hoc to paper The three-way taxonomy of spatio-temporal structures (serial, parallel, fusion) in Section 7 is exhaustive, and each method maps onto exactly one structure.
Equations 10-12 define the three categories, but no exclusion analysis is given for hybrid, Mamba, or LLM-assisted methods that interleave spatial and temporal modules.
domain assumption The four generalized modalities (Joint, Bone, Joint-Motion, Bone-Motion) and the four input representations (sequential, pseudo-image, graph, token) are the complete mainstream design space.
Sections 3.2 and 5 assert these as the mainstream categories; geometric features from Section 3.1 are set aside as a separate historical class without a formal boundary.

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Cite this review

Pith. "Pith review of 3D Skeleton-Based Action Recognition: A Review." pith.science (2026). https://pith.science/paper/EIWS6YGD

@misc{pith2026250600915,
  author       = {Pith},
  title        = {Pith review of: 3D Skeleton-Based Action Recognition: A Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EIWS6YGD}},
  note         = {Machine review of arXiv:2506.00915}
}

read the original abstract

With the inherent advantages of skeleton representation, 3D skeleton-based action recognition has become a prominent topic in the field of computer vision. However, previous reviews have predominantly adopted a model-oriented perspective, often neglecting the fundamental steps involved in skeleton-based action recognition. This oversight tends to ignore key components of skeleton-based action recognition beyond model design and has hindered deeper, more intrinsic understanding of the task. To bridge this gap, our review aims to address these limitations by presenting a comprehensive, task-oriented framework for understanding skeleton-based action recognition. We begin by decomposing the task into a series of sub-tasks, placing particular emphasis on preprocessing steps such as modality derivation and data augmentation. The subsequent discussion delves into critical sub-tasks, including feature extraction and spatio-temporal modeling techniques. Beyond foundational action recognition networks, recently advanced frameworks such as hybrid architectures, Mamba models, large language models (LLMs), and generative models have also been highlighted. Finally, a comprehensive overview of public 3D skeleton datasets is presented, accompanied by an analysis of state-of-the-art algorithms evaluated on these benchmarks. By integrating task-oriented discussions, comprehensive examinations of sub-tasks, and an emphasis on the latest advancements, our review provides a fundamental and accessible structured roadmap for understanding and advancing the field of 3D skeleton-based action recognition.

Figures

Figures reproduced from arXiv: 2506.00915 by the authors.

**Figure 1.** A comprehensive workflow for 3D skeleton-based action recognition using deep learning Skeleton Sequence Skeleton Pseudo-Image Feature … … t 4 5 9 8 3 2 6 7 1 1 10 11 4 2 4 4 1 4 2 4 4 1 6 2 6 6 1 6 2 6 6 1 8 2 8 8 1 8 2 8 8 joint fr a m e 1 ... ... ... ... ... ... ... 11 t11 . . .1 t. . .1 t. . .1 11 11 11 11 (a) CNN-based Feature Extraction Skeleton Sequence Skeleton Graph (c) Graph-based Feature Extraction … Featu… view at source ↗

**Figure 2.** Main feature extraction methods recently, hybrid methods [42, 43, 44, 45, 46] seek to combine the strengths of two or more backbone architectures, such as RNNs, CNNs, GCNs, and Transformers, to more comprehensively model both spatial and temporal aspects of skeleton data. Generative methods [47, 48, 49, 50] have been adapted to pre-train and regularize skeleton representations by reconstructing or predicting masked… view at source ↗

**Figure 3.** Chronological overview of the most relevant deep learning-based action recognition methods. and help readers delve into the core modeling challenges; (2) skeleton data preprocessing has a profound impact on subsequent spatio-temporal modeling and therefore warrants detailed examination; and (3) given the limited number of current surveys, our review must encompass the very latest developments, including Mamba archit… view at source ↗

Figures from the paper (6 more)

**Figure 5.** Figure 5: Eight feature types. Note that for each feature only the relevant joints, lines, and planes are drawn in red. f There are a few key differences between our method an [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: The pipeline of motion-adaptive data transformation. actionlets, applying extreme augmentations to non-actionlet regions, as shown in [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

**Figure 7.** Figure 7: Unstructured skeleton sequences are converted into four distinct input formats for further processing. challenge is to efficiently transform the sparse, unstructured skeleton action sequences into a structured format that deep learning models can understand while prese…

**Figure 2.** Figure 2: (a) Structure of HD-Graph with physically connected (PC) edges. The human skeleton graph is decomposed into a rooted tree, where PC edges are included in hierarchy sets. (b) Structure of HD-Graph with fully connected (FC) edges. Edges between all nodes in the same sema…

**Figure 10.** Figure 10: Architecture of the channel-wise topology refinement graph convolution. spatial modeling, demonstrating the potential of hybrid architectures in overcoming spatial modeling limitations. Spatial Feature Extraction Using CNNs. Convolutional Neural Networks (CNNs) exc…

**Figure 11.** Figure 11: Schematic diagram of three types of spatialtemporal modeling architecture. spatial or temporal features in isolation, neglecting the integration of both aspects. However, as the field has evolved, researchers have increasingly recognized the importance of jointly mo…

Discussion (0). Continue with ORCID to comment.

Reference graph

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