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arxiv: 2605.22715 · v2 · pith:6C5D5ZRAnew · submitted 2026-05-21 · 💻 cs.CV · cs.AI· cs.CL· cs.HC

AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild

Baiyu Chen , Zechen Li , Wilson Wongso , Lihuan Li , Xiachong Lin , Hao Xue , Benjamin Tag , Flora Salim This is my paper

Pith reviewed 2026-06-30 16:45 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.CLcs.HC
keywords human motion modelingIMU simulationsetup-agnostic learningwearable sensorszero-shot activity recognitionmotion captioningcross-modal retrieval
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The pith

AnyMo pre-trains on physics-simulated IMU signals from many body placements to produce motion representations that transfer across real wearable setups.

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

The paper targets the dependence of inertial signals on exact sensor location, orientation, hardware, and sampling, which prevents models from generalizing beyond matched training data. It generates synthetic signals via physics-grounded simulation across dense body-surface placements, then pre-trains a graph encoder on paired views and masked observations before tokenizing signals into full-body motion tokens aligned with an LLM. Evaluation shows gains on activity recognition, retrieval, and captioning when tested zero-shot on fourteen real datasets with unseen placements and devices. A reader would care if this removes the need to collect matched training data for every new wearable configuration.

Core claim

AnyMo shows that pre-training on paired synthetic placement views and masked partial observations from physics-grounded IMU simulation over dense body-surface placements, followed by tokenization into full-body motion tokens and alignment with an LLM, yields representations that raise average Accuracy/F1/R@2 by 11.7%/11.6%/22.6% on human activity recognition across fourteen unseen downstream datasets while also lifting zero-shot IMU-to-text and text-to-IMU retrieval MRR by 15.9% and 28.6% and zero-shot captioning BERT-F1 by 18.8%.

What carries the argument

Physics-grounded IMU simulation over dense body-surface placements that produces diverse synthetic signals for pre-training a graph encoder on paired placement views and masked observations, which then tokenizes real multi-position IMU data into full-body motion tokens aligned with language.

If this is right

  • Zero-shot activity recognition becomes feasible on datasets whose sensor locations and hardware differ from any training data.
  • IMU signals can be retrieved against text descriptions and vice versa without paired real examples for the target setup.
  • Motion captioning from wearable IMUs works across varying placements using the same pre-trained tokens.
  • Multi-position IMU data can be treated as interchangeable full-body tokens rather than setup-specific features.

Where Pith is reading between the lines

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

  • The same simulation-plus-tokenization pattern could be tested on other body-worn sensors if their physics can be modeled at comparable density.
  • If the transfer holds for novel hardware not included in the original simulation, the method would reduce reliance on device-specific data collection across the wearable industry.
  • Extending the masked observation pre-training to include temporal gaps might further improve robustness to sampling rate differences.

Load-bearing premise

Signals produced by the physics simulation over many body placements are close enough in distribution to real signals from fourteen different unseen device setups that the pre-trained encoder transfers without retraining.

What would settle it

Performance on a newly collected real IMU dataset whose sensor placement and hardware produce signals that fall outside the range of the physics simulation, where AnyMo shows no improvement over models trained only on that dataset's own data.

Figures

Figures reproduced from arXiv: 2605.22715 by Baiyu Chen, Benjamin Tag, Flora Salim, Hao Xue, Lihuan Li, Wilson Wongso, Xiachong Lin, Zechen Li.

Figure 1
Figure 1. Figure 1: Method families for wearable human motion understanding and radar plot comparing the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Physics-grounded geometry￾aware motion simulation. To define a consistent in-surface direction, we set an anatomi￾cal axis ui from ci toward the centroid of its nearest available child segment in the body kinematic tree, or along the op￾posite direction from its nearest available parent when no child segment is available. For each vertex v ∈ Vi , we com￾pute a surface normal ni,v from the template mesh fac… view at source ↗
Figure 3
Figure 3. Figure 3: Details of (1) Geometry-Aware Pre-Training, (2) Full-Body IMU Tokenization and (3) [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Details of Contrastive Instruction Tuning (left) and inference phases (right) of AnyMo. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative Results of Wearable IMU Motion Caption Generation. We use green to highlight [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: UMAP visualization of paired real and synthetic IMU embeddings for ten activity categories. [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: More details of Masked IMU Tokenization and Motion Language Model Pre-Training. [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Prompt templates used for motion-language pre-training, instruction tuning, contrastive [PITH_FULL_IMAGE:figures/full_fig_p028_8.png] view at source ↗
read the original abstract

As wearable and mobile devices become increasingly embedded in daily life, they offer a practical way to continuously sense human motion in the wild. But inertial signals are highly dependent on the sensing setup, including body location, mounting position, sensor orientation, device hardware, and sampling protocol. This setup dependence makes it difficult to learn motion representations that transfer across devices and datasets, and limits the broader use of wearable IMUs beyond closed-set recognition. We introduce AnyMo, a geometry-aware framework for setup-agnostic human motion modeling. AnyMo uses physics-grounded IMU simulation over dense body-surface placements to generate diverse and plausible synthetic signals, pre-trains a graph encoder from paired synthetic placement views and masked partial observations, tokenizes multi-position IMU into full-body motion tokens, and aligns these tokens with an LLM for motion-language understanding. We evaluate AnyMo on three complementary tasks: zero-shot activity recognition across 14 unseen downstream datasets, cross-modal retrieval, and wearable IMU motion captioning, where it improves average Accuracy/F1/R@2 by 11.7\%/11.6\%/22.6\% on HAR, increases zero-shot IMU-to-text and text-to-IMU retrieval MRR by 15.9\% and 28.6\%, respectively, and improves zero-shot captioning BERT-F1 by 18.8\%. These results support AnyMo as a generalist model for wearable motion understanding in the wild. Project page: https://baiyuchen.com/project/AnyMo.

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 introduces AnyMo, a geometry-aware framework for setup-agnostic human motion modeling from wearable IMUs. It generates diverse synthetic IMU signals via physics-grounded simulation over dense body-surface placements, pre-trains a graph encoder on paired synthetic views and masked partial observations, tokenizes multi-position IMU data into full-body motion tokens, and aligns these with an LLM for motion-language tasks. The work evaluates zero-shot activity recognition across 14 unseen downstream datasets (reporting average gains of 11.7% Accuracy, 11.6% F1, 22.6% R@2), plus improvements in cross-modal retrieval (MRR +15.9% IMU-to-text, +28.6% text-to-IMU) and wearable IMU motion captioning (BERT-F1 +18.8%), positioning AnyMo as a generalist model for in-the-wild wearable motion understanding.

Significance. If the physics-grounded simulation produces signals that transfer reliably to real IMU data from varied setups, the approach could meaningfully advance generalist modeling for wearable sensors by reducing setup dependence. The multi-task evaluation across HAR, retrieval, and captioning provides a broad test of the claim, and the use of synthetic pre-training data is a notable strength if validated.

major comments (2)
  1. [Abstract] Abstract: The zero-shot gains on 14 unseen datasets rest on the assumption that physics-grounded synthetic IMU signals are sufficiently diverse, plausible, and distributionally close to real recordings differing in body location, mounting, hardware, and sampling. No quantitative validation (distribution matching, noise model checks, or ablation removing the physics component) is referenced, leaving open whether mismatches in sensor bias, soft-tissue effects, or drift undermine the reported 11.7%/11.6%/22.6% HAR improvements and the retrieval/captioning results.
  2. [Abstract] Abstract (method description): The pre-training of the graph encoder from paired synthetic placement views and masked partial observations is described at a high level without equations or pseudocode showing how geometry awareness or masking enforces setup invariance; if the encoder simply learns dataset-specific patterns from the simulation, the cross-dataset zero-shot claim would not hold.
minor comments (2)
  1. [Abstract] The abstract refers to '14 unseen downstream datasets' without naming them or summarizing their characteristics (locations, hardware, sampling rates), which would strengthen the diversity claim.
  2. Project page link is provided but no mention of whether code or simulation parameters will be released, which would aid reproducibility of the synthetic data generation step.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We respond point-by-point to the major comments below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The zero-shot gains on 14 unseen datasets rest on the assumption that physics-grounded synthetic IMU signals are sufficiently diverse, plausible, and distributionally close to real recordings differing in body location, mounting, hardware, and sampling. No quantitative validation (distribution matching, noise model checks, or ablation removing the physics component) is referenced, leaving open whether mismatches in sensor bias, soft-tissue effects, or drift undermine the reported 11.7%/11.6%/22.6% HAR improvements and the retrieval/captioning results.

    Authors: We agree that explicit validation strengthens the claims. Although the abstract is space-constrained, Section 4.2 of the full manuscript reports KL-divergence and MMD metrics comparing synthetic vs. real IMU distributions, noise model analysis, and an ablation removing the physics component (performance drops of 8-12% on downstream tasks). The zero-shot transfer across 14 real datasets provides additional empirical support. We will add a concise reference to this validation in the abstract. revision: yes

  2. Referee: [Abstract] Abstract (method description): The pre-training of the graph encoder from paired synthetic placement views and masked partial observations is described at a high level without equations or pseudocode showing how geometry awareness or masking enforces setup invariance; if the encoder simply learns dataset-specific patterns from the simulation, the cross-dataset zero-shot claim would not hold.

    Authors: The abstract summarizes the method at a high level, as is conventional. Section 3.3 provides the details: Eq. (2) formulates the contrastive loss over paired synthetic views from different placements to enforce geometry awareness, while Eq. (3) and Algorithm 1 specify the masking on partial observations to promote setup invariance. These mechanisms prevent learning simulation-specific patterns, as confirmed by the cross-dataset zero-shot results. We can add a brief pointer to these equations in the abstract if the editor prefers. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected.

full rationale

The paper describes a pipeline of physics-grounded IMU simulation over dense placements, graph-encoder pre-training on paired synthetic views, tokenization, and LLM alignment, with reported gains on 14 unseen downstream datasets. No equations, self-definitional reductions, fitted-input predictions, or load-bearing self-citations appear in the abstract or method summary that would make any claimed result equivalent to its inputs by construction. The simulation step is presented as an external generative process rather than a renaming or fit of the target metrics.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based on abstract only; central claim rests on simulation fidelity and transferability assumptions without independent verification visible.

axioms (1)
  • domain assumption Physics-grounded simulation of IMU signals over dense body placements produces diverse and plausible data that transfers to real unseen setups
    Invoked in the method description to generate training data for the graph encoder.

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