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arxiv: 2603.18003 · v3 · submitted 2026-03-18 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

Universal Skeleton Understanding via Differentiable Rendering and MLLMs

Ziyi Wang , Peiming Li , Xinshun Wang , Yang Tang , Kai-Kuang Ma , Mengyuan Liu
Authors on Pith no claims yet

Pith reviewed 2026-05-15 09:21 UTC · model grok-4.3

classification 💻 cs.CV
keywords skeleton understandingdifferentiable renderingmultimodal large language modelsaction recognitionmotion captioningopen-vocabulary learning
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The pith

Differentiable rendering converts arbitrary skeleton sequences into images that MLLMs can process directly.

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

The paper presents SkeletonLLM to give multimodal large language models access to human skeleton data that normally lies outside their visual and text inputs. A differentiable renderer called DrAction turns any skeleton sequence into compact image sequences, and because the whole pipeline stays end-to-end differentiable the MLLM can steer the renderer toward task-relevant visuals. Cooperative training combines reasoning distillation from a teacher model with discriminative fine-tuning to sharpen distinctions between similar actions. The result is strong open-vocabulary action recognition plus natural extension to motion captioning and question answering on skeleton inputs that differ in format and length.

Core claim

SkeletonLLM achieves universal skeleton understanding by translating arbitrary skeleton sequences into the MLLM's native visual modality via DrAction, a differentiable, format-agnostic renderer that converts skeletal kinematics into compact image sequences. MLLM gradients directly guide the rendering to produce task-informative visual tokens, and a cooperative training strategy using causal reasoning distillation and discriminative finetuning enables strong generalization in open-vocabulary action recognition while extending reasoning to motion captioning and QA across heterogeneous skeleton formats.

What carries the argument

DrAction, a differentiable renderer that converts skeletal kinematics into compact image sequences so MLLM gradients can directly optimize the produced visual tokens.

If this is right

  • Strong generalization to open-vocabulary action recognition holds across skeleton formats that were never seen together during training.
  • Reasoning capabilities transfer directly to motion captioning without additional task-specific heads.
  • Question answering on skeleton data works for inputs that differ in joint count, frame rate, and coordinate system.
  • The same pipeline supplies a route for applying MLLMs to other non-visual structured sequences by converting them to images.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same differentiable-rendering route could adapt MLLMs to other kinematic signals such as facial landmarks or animal poses with only a change of the renderer.
  • Differentiability may prove essential for letting language models ingest graph-structured or time-series data that lack native visual form.
  • Scaling the renderer to higher-resolution or longer sequences would test whether information loss stays negligible at larger motion scales.

Load-bearing premise

Rendering skeleton kinematics into compact image sequences preserves all task-relevant motion information without meaningful loss.

What would settle it

An experiment in which the rendered image sequences cause the model to confuse two actions that remain clearly separable when the same model is given the raw skeleton coordinates directly would show that critical information is lost in the rendering step.

Figures

Figures reproduced from arXiv: 2603.18003 by Kai-Kuang Ma, Mengyuan Liu, Peiming Li, Xinshun Wang, Yang Tang, Ziyi Wang.

Figure 1
Figure 1. Figure 1: Breaking Format Silos and the Modality Gap. (Top) MLLMs possess strong reasoning capabilities but cannot natively process structured skeleton data. (Middle) Traditional alignment methods are tied to specific skeleton topologies, compressing mo￾tion into a single vector for matching against text embeddings, which creates representation bottlenecks and brittle semantics. (Bottom) Our SkeletonLLM uses DrActio… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of SkeletonLLM. The pipeline follows a Render-Reason-Respond process for universal understanding. Given a skeleton sequence, DrAction lifts joint trajectories into deformable 3D Gaussian primitives and renders motion-aware images. Joint transforms are computed via Linear Blend Skinning, and kinematic cues (depth, velocity) are fused through a Neural Feature Modulator. All parameters are optimized … view at source ↗
Figure 3
Figure 3. Figure 3: Cross-format rendering by DrAction. Top row: DrAc￾tion renders skeletons from four different formats into visually consistent image sequences. Bottom row: the underlying skeleton topologies vary significantly in joint count and connectivity—NW￾UCLA (Kinect v1, 20 joints), NTU (Kinect v2, 25 joints), NTU-2D (pose estimation, 17 joints), and HumanML3D (MoCap, 22 joints). Despite these differences, DrAction p… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison of rendering methods. Fixed renderers (3D+Velocity, 2D, JTM (Wang et al., 2016b)) produce visualizations that are either generic, information-poor, or percep￾tually complex. DrAction learns an abstract representation. With the NFM, it dynamically highlights kinematically salient regions (e.g., the kicking leg), producing a more informative visual lan￾guage for the MLLM. A Video Galle… view at source ↗
Figure 5
Figure 5. Figure 5: Our Progressive Training Pipeline. To address the joint optimization challenge, we progressively activate and fine-tune model components. The training curriculum begins with (a) warming up the renderer to generate intelligible visuals and concludes with (d) refining recognition, both utilizing a multiple-choice question & answer (MQA) task. In between, the strategy incorporates (b) learning discriminative … view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative comparison of different rendering methods. Each column shows a representative action instance. From top to bottom, we compare three fixed renderers (3D+Velocity, 2D projection, and JTM (Wang et al., 2016b)) with our learnable DrAction variants (w/o NFM and full DrAction). Static methods often miss or clutter fine-grained motion cues. In contrast, DrAction, particularly when enhanced by the Neur… view at source ↗
Figure 7
Figure 7. Figure 7: Impact of rendered frame count. Accuracy on NTU-60 increases sharply from 4 to 10 frames and saturates at 12 frames (59.02%). Increasing to 16 frames yields minimal gains (59.45%), making 12 frames the optimal trade-off. terization details, it incurs disproportionately higher mem￾ory and computational costs. We therefore standardize on 448×448 to achieve the best balance between accuracy and efficiency for… view at source ↗
Figure 8
Figure 8. Figure 8: Confusion matrix comparison on the NTU-60 (48/12 split). Left: InternVL3 baseline. Right: SkeletonLLM (Ours). Our method significantly reduces confusion between visually similar actions (highlighted in yellow) and improves accuracy on fine-grained classes (red boxes), demonstrating the effectiveness of Discriminative Finetuning. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Comparison of reasoning processes on the NTU-60 (48/12 split). We visualize the chain-of-thought generated by SkeletonLLM w/o Disc-FT & CR-Distill (top) and the full SkeletonLLM (bottom) for a “Put on shoe” sequence. While the ablated variant hallucinates a “Pick up” action based on a coarse bending posture, the full SkeletonLLM accurately identifies fine-grained hand-foot interactions (red text) and emplo… view at source ↗
Figure 10
Figure 10. Figure 10: t-SNE visualization of feature representations on NTU-60 (48/12 split). The visualization is derived from the 48/12 open-vocabulary split (Xsub benchmark). Points in faded, lighter colors represent the 48 seen classes, while the darker, highlighted clusters correspond to the 12 unseen classes. Even though the unseen actions were never part of the training set, their samples spontaneously organize into com… view at source ↗
Figure 11
Figure 11. Figure 11: Qualitative comparison of reasoning processes. Given a “headache” sequence rendered by DrAction, we compare outputs from different models on a multiple-choice QA task. MotionGPT captures only coarse patterns and selects “neck pain.” InternVL3 (fixed render) and SkeletonLLM w/o CR-Distill both misinterpret the gesture as a “salute,” focusing on superficial posture cues (“upright and stable,” “hand at brow … view at source ↗
Figure 12
Figure 12. Figure 12: Prompt templates for MQA, Disc-FT, and CR-Distill. (Top-left) Prompt template for the MQA task. (Bottom-left) Prompt template for Disc-FT. (Top-right) Teacher prompt for CR-Distill. (Bottom-right) Student prompt for CR-Distill. 28 [PITH_FULL_IMAGE:figures/full_fig_p028_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Top-5 MLLM-mined semantically similar actions for each class on NTU-60, used as hard-negative candidates in Disc-FT. Actions sharing similar body-part involvement form natural clusters (e.g., “clapping,” “rub two hands,” “pray with hands together,” “high-five”); confusing pairs often differ only in subtle temporal or spatial cues (e.g., “put on shoe” vs. “take off shoe,” “sit down” vs. “stand up”). 30 [P… view at source ↗
Figure 14
Figure 14. Figure 14: Top-5 MLLM-mined semantically similar actions for each class on NTU-120, used as hard-negative candidates in Disc-FT. 31 [PITH_FULL_IMAGE:figures/full_fig_p031_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Examples of teacher-generated causal reasoning chains used in CR-Distill. 32 [PITH_FULL_IMAGE:figures/full_fig_p032_15.png] view at source ↗
read the original abstract

Multimodal large language models (MLLMs) exhibit strong visual-language reasoning, yet remain confined to their native modalities and cannot directly process structured, non-visual data such as human skeletons. Existing methods either compress skeleton dynamics into lossy feature vectors for text alignment, or quantize motion into discrete tokens that generalize poorly across heterogeneous skeleton formats. We present SkeletonLLM, which achieves universal skeleton understanding by translating arbitrary skeleton sequences into the MLLM's native visual modality. At its core is DrAction, a differentiable, format-agnostic renderer that converts skeletal kinematics into compact image sequences. Because the pipeline is end-to-end differentiable, MLLM gradients can directly guide the rendering to produce task-informative visual tokens. To further enhance reasoning capabilities, we introduce a cooperative training strategy: Causal Reasoning Distillation transfers structured, step-by-step reasoning from a teacher model, while Discriminative Finetuning sharpens decision boundaries between confusable actions. SkeletonLLM demonstrates strong generalization in open-vocabulary action recognition, while its learned reasoning capabilities naturally extend to motion captioning and question answering across heterogeneous skeleton formats -- suggesting a viable path for applying MLLMs to non-native modalities. Code will be released upon acceptance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes SkeletonLLM to enable MLLMs to process arbitrary human skeleton sequences by translating them into the models' native visual modality via DrAction, a differentiable format-agnostic renderer that produces compact image sequences. End-to-end differentiability allows MLLM gradients to guide rendering, and a cooperative training strategy (causal reasoning distillation plus discriminative finetuning) is introduced to support open-vocabulary action recognition as well as motion captioning and QA across heterogeneous skeleton formats.

Significance. If the empirical claims hold, the work would provide a practical route for extending MLLMs to structured non-visual data without format-specific tokenizers or lossy feature compression. The explicit use of differentiable rendering to close the modality gap and the cooperative training for structured reasoning are potentially valuable contributions to multimodal learning and human motion understanding.

major comments (2)
  1. [Abstract] Abstract: the claim that SkeletonLLM 'demonstrates strong generalization in open-vocabulary action recognition' and extends naturally to captioning/QA is unsupported by any quantitative results, ablation studies, error analysis, or baseline comparisons, which are load-bearing for the central empirical claims.
  2. [DrAction] DrAction description: the assertion that differentiable rendering of skeleton kinematics into compact image sequences preserves all task-relevant information (joint angles, velocities, 3D structure) without significant loss is not verified; standard 2D projections can discard depth and blur temporal dynamics, and no ablations on confusable actions or cross-format transfer are supplied to confirm retained fidelity.
minor comments (2)
  1. [Abstract] The acronym 'DrAction' is introduced without an explicit expansion or reference to its full name on first use.
  2. The manuscript promises code release but provides no details on data splits, training hyperparameters, or evaluation protocols that would support reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive feedback on our manuscript. We address each major comment below and will revise the paper to strengthen the empirical support as requested.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that SkeletonLLM 'demonstrates strong generalization in open-vocabulary action recognition' and extends naturally to captioning/QA is unsupported by any quantitative results, ablation studies, error analysis, or baseline comparisons, which are load-bearing for the central empirical claims.

    Authors: We agree that the abstract claims require more explicit quantitative backing to be fully supported. The main body of the manuscript (Section 4) already contains quantitative results for open-vocabulary action recognition, including accuracy metrics, baseline comparisons (e.g., against feature-compression and tokenization methods), and cross-format transfer experiments on multiple skeleton datasets. However, the extensions to captioning and QA are supported primarily through qualitative examples and case studies rather than full quantitative tables or error analysis. In the revised manuscript we will add quantitative metrics (e.g., BLEU, CIDEr for captioning; accuracy for QA), additional ablations, and error analysis to directly substantiate the generalization claims. revision: yes

  2. Referee: [DrAction] DrAction description: the assertion that differentiable rendering of skeleton kinematics into compact image sequences preserves all task-relevant information (joint angles, velocities, 3D structure) without significant loss is not verified; standard 2D projections can discard depth and blur temporal dynamics, and no ablations on confusable actions or cross-format transfer are supplied to confirm retained fidelity.

    Authors: We acknowledge the need for explicit verification of information preservation. DrAction employs 3D-aware multi-view projections combined with temporal stacking and velocity encoding to mitigate depth loss and motion blur, and the end-to-end differentiability allows task gradients to optimize for retained information. While the current manuscript includes some fidelity checks via reconstruction error and downstream task performance, we did not provide dedicated ablations isolating confusable action pairs or systematic cross-format transfer for DrAction itself. In the revision we will add these targeted ablations (including confusion matrices for similar actions and direct comparisons of rendered sequences across skeleton formats) to empirically confirm that task-relevant kinematics are preserved. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain; empirical pipeline is self-contained

full rationale

The paper describes an empirical pipeline (DrAction differentiable rendering plus cooperative training) without presenting equations or derivations that reduce claimed performance or generalization to quantities defined by fitted parameters or self-referential constructions within the work. Central claims rest on experimental outcomes for open-vocabulary recognition, captioning, and QA across formats, which are externally falsifiable via benchmarks and ablations rather than tautological by construction. No self-definitional loops, fitted inputs renamed as predictions, load-bearing self-citations, or ansatz smuggling appear in the text; the approach remains open to independent validation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the unproven premise that image-based rendering of skeletons is sufficiently lossless for downstream reasoning tasks and that end-to-end differentiability will produce task-informative tokens without additional constraints.

axioms (1)
  • domain assumption Skeleton kinematics can be losslessly or near-losslessly represented as compact image sequences for MLLM processing
    Invoked in the description of DrAction as the core translation mechanism.
invented entities (1)
  • DrAction no independent evidence
    purpose: Differentiable, format-agnostic renderer that converts skeletal kinematics into image sequences
    New component introduced to bridge skeleton data to visual modality

pith-pipeline@v0.9.0 · 5527 in / 1281 out tokens · 28986 ms · 2026-05-15T09:21:07.795431+00:00 · methodology

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Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages · 4 internal anchors

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    Discriminative training precedes generative reasoning to establish robust feature boundaries

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    clapping

    The final stage consolidates all learned capabilities with- out disrupting the renderer’s learned representations. A.1.4. SUMMARY OFCOMPONENTCONTRIBUTIONS Table 9 summarizes the role of each component in address- ing the challenges of skeleton-MLLM integration. A.1.5. QUANTITATIVEABLATION ONPROGRESSIVE TRAINING We conduct a comprehensive ablation study to...

    work page arXiv 2044

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    put on shoe

    Providing the teacher model with the ground-truth label and distilling the full rationale including the label yields the best performance. Setup NTU-60 (%) NTU-120 (%) 55/5 48/12 110/10 96/24 w/o CR-Distill 85.27 63.37 74.68 64.97 w/o Cond. Label 86.67 64.58 75.53 66.59 w/o Final Label 85.47 63.77 75.58 65.78 Ours (Full) 87.37 64.72 76.05 67.20 B.1. Resul...

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    Rendering all skeletons through the same differentiable rasterization pipeline with identical camera parameters

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    Using the Neural Feature Modulator (NFM) to pro- duce task-optimized appearances that emphasize motion- salient regions rather than skeleton-specific details

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    What is the first step of this action?

    Blending learned colors with depth-based visualization to maintain spatial coherence across formats. This creates avisual lingua francathat the MLLM can interpret uniformly, enabling seamless cross-format transfer: a model trained on Kinect skeletons (25 joints) can directly process MoCap data (22 joints) or 2D pose estimations (17 joints) without any arc...

    work page 2024

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    YES” or “NO

    with a 0.03 warm-up ratio. The four training stages are trained for 1, 1, 1, and 3 epochs, respectively. For eval- uation, we conduct a single test run where the predicted label is compared against the ground-truth after standard text normalization (lowercasing and whitespace trimming). E.2. DrAction Implementation This section provides a detailed exposit...

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    clapping,

    with all other class names and ask it to rank which actions are most similar to y in terms of body parts involved and motion patterns. We then retain the top-5 most similar actions as candidate negatives for y. The mined neighbors for NTU-60 and NTU-120 are visualized in Figures 13 and 14. Several consistent patterns emerge: actions sharing similar body-p...

    work page 2024