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arxiv: 2604.09715 · v1 · submitted 2026-04-08 · 💻 cs.CV · eess.IV

Recognition: no theorem link

MuPPet: Multi-person 2D-to-3D Pose Lifting

Thomas Markhorst , Zhi-Yi Lin , Jouh Yeong Chew , Jan van Gemert , Xucong Zhang
Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:14 UTC · model grok-4.3

classification 💻 cs.CV eess.IV
keywords multi-person pose estimation2D-to-3D liftinginter-person correlationsocclusion handlingattention mechanismsgroup interactionshuman pose estimation
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The pith

Explicitly modeling relationships between people allows more accurate lifting of their 2D poses into 3D, especially when some are occluded.

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

The paper sets out to demonstrate that 2D-to-3D pose lifting works better in groups when the model accounts for how the individuals relate to one another rather than treating each person in isolation. Existing approaches either ignore these links or cannot scale to different numbers of people in a scene. MuPPet adds three targeted pieces: an encoding step that organizes each person's features, a training step that permutes people to create more varied examples, and an attention step that lets the model weigh connections between people dynamically. Tests on datasets showing group interactions confirm higher accuracy than prior single-person and multi-person methods, with particular gains when bodies block one another.

Core claim

MuPPet is a multi-person 2D-to-3D pose lifting framework that explicitly models inter-person correlations. It does so through Person Encoding to structure individual representations, Permutation Augmentation to enhance training diversity, and Dynamic Multi-Person Attention to adaptively model correlations between individuals. Extensive experiments on group interaction datasets show that this approach significantly outperforms state-of-the-art single- and multi-person 2D-to-3D pose lifting methods while improving robustness in occlusion scenarios.

What carries the argument

Dynamic Multi-Person Attention that adaptively models correlations between individuals, supported by Person Encoding to structure representations and Permutation Augmentation to increase training variety.

If this is right

  • The model can process scenes with any number of people without requiring a fixed group size.
  • Accuracy holds up better when one or more individuals are partially hidden from the camera.
  • The resulting 3D poses carry richer information about social spatial arrangements.
  • Downstream tasks that rely on group pose, such as interaction analysis, receive more reliable input.

Where Pith is reading between the lines

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

  • Extending the same attention structure to video frames could enforce consistency across time without major redesign.
  • The emphasis on group context may transfer to related problems like multi-person tracking or collective activity recognition.
  • Real-world deployment would benefit from checking performance on crowds larger than those in current benchmark datasets.

Load-bearing premise

That adding explicit modeling of inter-person relationships through encoding, permutation, and attention will consistently raise 3D accuracy and handle changing group sizes and occlusions better than single-person methods.

What would settle it

A new test set containing large groups with frequent mutual occlusions on which MuPPet fails to exceed the accuracy of the strongest single-person lifting baseline.

Figures

Figures reproduced from arXiv: 2604.09715 by Jan van Gemert, Jouh Yeong Chew, Thomas Markhorst, Xucong Zhang, Zhi-Yi Lin.

Figure 1
Figure 1. Figure 1: We exploit social inter-person correlations for 3D pose [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our MuPPet pipeline. Given a sequence of detected 2D human pose joints from multiple persons [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Example of permutation learning for a five person scene. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: We show the performance of different levels of occlusion on the Haggling dataset, in comparison with the adapted D3DP [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative results on an in-the-wild setting predicted by [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
read the original abstract

Multi-person social interactions are inherently built on coherence and relationships among all individuals within the group, making multi-person localization and body pose estimation essential to understanding these social dynamics. One promising approach is 2D-to-3D pose lifting which provides a 3D human pose consisting of rich spatial details by building on the significant advances in 2D pose estimation. However, the existing 2D-to-3D pose lifting methods often neglect inter-person relationships or cannot handle varying group sizes, limiting their effectiveness in multi-person settings. We propose MuPPet, a novel multi-person 2D-to-3D pose lifting framework that explicitly models inter-person correlations. To leverage these inter-person dependencies, our approach introduces Person Encoding to structure individual representations, Permutation Augmentation to enhance training diversity, and Dynamic Multi-Person Attention to adaptively model correlations between individuals. Extensive experiments on group interaction datasets demonstrate MuPPet significantly outperforms state-of-the-art single- and multi-person 2D-to-3D pose lifting methods, and improves robustness in occlusion scenarios. Our findings highlight the importance of modeling inter-person correlations, paving the way for accurate and socially-aware 3D pose estimation. Our code is available at: https://github.com/Thomas-Markhorst/MuPPet

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 MuPPet, a multi-person 2D-to-3D pose lifting framework that explicitly models inter-person correlations via three introduced components: Person Encoding to structure individual representations, Permutation Augmentation to enhance training diversity, and Dynamic Multi-Person Attention to adaptively model correlations between individuals. It evaluates the approach on group interaction datasets and claims significant outperformance over state-of-the-art single- and multi-person 2D-to-3D lifting methods along with improved robustness under occlusion.

Significance. If the empirical results hold with proper validation, the work would be significant for the field by shifting 2D-to-3D lifting from single-person assumptions to explicit inter-person modeling, which is relevant for social scene understanding and related applications. The release of code at the provided GitHub link supports reproducibility.

major comments (2)
  1. [Abstract and Experiments] Abstract and Experiments section: The central claim that MuPPet 'significantly outperforms' SOTA methods and improves occlusion robustness is asserted without any quantitative metrics (e.g., MPJPE or PCK), ablation results isolating the contribution of each of the three components, dataset details, or error analysis. This is load-bearing for the empirical claim and prevents verification that the data supports the assertions.
  2. [Method] Method section (Dynamic Multi-Person Attention): The description of how the attention mechanism handles varying group sizes (e.g., via masking, padding, or fixed-size assumptions) is not specified in sufficient detail to assess whether it generalizes as claimed beyond the tested datasets.
minor comments (2)
  1. [Abstract] The abstract would benefit from including one or two key quantitative results and the names of the group interaction datasets used to make the contribution clearer at a glance.
  2. [Figures and Tables] Ensure that all figures (e.g., architecture diagrams) include clear labels for the three proposed components and that tables compare against both single-person and multi-person baselines with consistent metrics.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the presentation of our empirical results and methodological details. We address each point below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract and Experiments] Abstract and Experiments section: The central claim that MuPPet 'significantly outperforms' SOTA methods and improves occlusion robustness is asserted without any quantitative metrics (e.g., MPJPE or PCK), ablation results isolating the contribution of each of the three components, dataset details, or error analysis. This is load-bearing for the empirical claim and prevents verification that the data supports the assertions.

    Authors: We agree that the abstract states the performance claim at a high level without numbers (standard for abstracts) and that the Experiments section would benefit from greater explicitness. The current version includes MPJPE results on group interaction datasets and some comparisons, but we acknowledge the absence of dedicated ablations isolating Person Encoding, Permutation Augmentation, and Dynamic Multi-Person Attention, as well as limited dataset statistics and occlusion-specific error analysis. In the revision we will add: (1) a summary table of key MPJPE/PCK numbers in the Experiments section, (2) an ablation study subsection quantifying each component's contribution, (3) expanded dataset descriptions (including group size distributions and occlusion statistics), and (4) error analysis focused on occlusion robustness. These changes will make the supporting evidence fully verifiable. revision: yes

  2. Referee: [Method] Method section (Dynamic Multi-Person Attention): The description of how the attention mechanism handles varying group sizes (e.g., via masking, padding, or fixed-size assumptions) is not specified in sufficient detail to assess whether it generalizes as claimed beyond the tested datasets.

    Authors: We thank the referee for highlighting this gap in clarity. The Dynamic Multi-Person Attention handles variable group sizes by padding sequences to a fixed maximum length and applying a binary mask that excludes padded tokens from the attention computation (preventing any fixed-size assumption on the actual number of people). We will revise the Method section to include a precise description of the padding and masking procedure, the corresponding equations, and a short pseudocode snippet. This will explicitly demonstrate generalization to arbitrary group sizes within the tested range and beyond. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper introduces MuPPet as a new framework that explicitly adds three components—Person Encoding, Permutation Augmentation, and Dynamic Multi-Person Attention—to model inter-person correlations on top of existing 2D pose estimators. These additions are presented as novel remedies to limitations of prior single-person lifting methods, with performance gains shown via experiments on group datasets. No derivation step reduces by construction to a fitted parameter, self-definition, or a load-bearing self-citation chain; the central claims rest on the independent design of the new modules and external empirical validation rather than renaming or re-deriving inputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Only the abstract is available, so the ledger is limited; the approach rests on the domain assumption that inter-person correlations are load-bearing for accuracy and that the three introduced modules can capture them effectively.

free parameters (1)
  • Neural network hyperparameters
    Training of attention-based models typically involves multiple hyperparameters chosen to fit the group interaction datasets.
axioms (1)
  • domain assumption Inter-person relationships improve 3D pose lifting accuracy in group settings
    The framework is built around this premise to justify the new components.

pith-pipeline@v0.9.0 · 5544 in / 1401 out tokens · 39014 ms · 2026-05-10T19:14:12.856627+00:00 · methodology

discussion (0)

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