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arxiv: 2510.02043 · v2 · submitted 2025-10-02 · 💻 cs.CV · cs.HC· cs.LG

Zero-shot Human Pose Estimation using Diffusion-based Inverse solvers

Sahil Bhandary Karnoor , Romit Roy Choudhury This is my paper

Pith reviewed 2026-05-18 10:21 UTC · model grok-4.3

classification 💻 cs.CV cs.HCcs.LG
keywords human pose estimationdiffusion modelszero-shot learninginverse problemswearable sensorsgeneralizationmotion tracking
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The pith

A pre-trained diffusion model conditioned on rotations and guided by location likelihoods enables zero-shot pose estimation across users.

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

The paper tries to establish that pose estimation can generalize to new users without retraining by reformulating it as an inverse problem solved with diffusion models. Instead of conditioning on both location and rotation measurements, which vary with body size, the method conditions the model only on rotations and uses locations to guide the generation via a likelihood term. This would matter to a reader because it allows practical systems with few on-body sensors to work for anyone without collecting user-specific data. The approach leverages the fact that rotational measurements are less affected by individual body differences than positional ones.

Core claim

We formulate pose estimation as an inverse problem and design an algorithm capable of zero-shot generalization. Our idea utilizes a pre-trained diffusion model and conditions it on rotational measurements alone; the priors from this model are then guided by a likelihood term, derived from the measured locations. Thus, given any user, our proposed InPose method generatively estimates the highly likely sequence of poses that best explains the sparse on-body measurements.

What carries the argument

InPose, a diffusion-based inverse solver that conditions a pre-trained diffusion model on rotational measurements and guides it with a likelihood derived from measured locations.

If this is right

  • Pose tracking systems can operate without per-user training or calibration data.
  • Full body posture can be estimated from sparse sensors placed on the body.
  • Generative sampling produces likely pose sequences that fit the measurements.
  • Generalization improves because location measurements no longer directly condition the model.
  • Zero-shot performance becomes feasible for users with varying body sizes.

Where Pith is reading between the lines

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

  • Similar inverse solver techniques could apply to other sensor fusion problems where some measurements are user-specific and others are general.
  • Testing on datasets with extreme body size variations would reveal the limits of the rotation-only conditioning.
  • The method implies that human pose priors in diffusion models are robust enough to transfer across individuals when properly guided.

Load-bearing premise

The pre-trained diffusion model provides sufficiently accurate priors for natural human poses for a target user with different body size even without receiving location information during conditioning.

What would settle it

If the poses generated by InPose for a new user consistently fail to match the actual measured locations when evaluated on a diverse test set with varying body proportions, the zero-shot generalization claim would be falsified.

Figures

Figures reproduced from arXiv: 2510.02043 by Romit Roy Choudhury, Sahil Bhandary Karnoor.

Figure 1
Figure 1. Figure 1: (a) InPose’s input and output visualized over 4 time frames. (b) “T” pose. (c) Pose with depiction of rotation angle and root translation. InPose’s inverse problem formulation can be sketched as follows. We train a Diffusion model conditioned on rotational measurements from existing datasets; this gives us a conditional prior on scale-free poses. When inferring a specific user’s pose, we use the user’s bod… view at source ↗
Figure 2
Figure 2. Figure 2: InPose pipeline: 3-sensor rotation + location measurements are inputs. Rotations fed as conditions to CFG which outputs conditional prior; location measurements estimate the likelihood, which steers denoising. tor in Eq. 1 becomes linear. When l1 = 0, the joint location lj becomes a matrix-vector product, Cκ, as follows: [R1...Rpj ] · [b ⊤ 2,1 ...b⊤ j,pj ] ⊤ = lj (8) where C = [R1...Rpj ], and κ = [b ⊤ 2,1… view at source ↗
Figure 3
Figure 3. Figure 3: (a) Position error vs. body scale. (b) Rotation error vs. body scale. (c) Position error vs. location noise. All these tests were performed using Protocol 1 Robustness to measurement noise: InPose is designed to be implicitly robust to location mea￾surement noise as well. We inject zero-mean i.i.d. Gaussian noise into the input location streams and compute the estimation errors, while maintaining the defau… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative results with scaling body size. The same pose is used for all scales. Qualitative results with scaling: Fig.4 presents qualitative comparisons between InPose and BoDiffusion(Global), for the default body size and two scaling factors of 0.6 and 1.4. BoDif￾fusion performs better for the default size, es￾pecially in the lower body, but degrades at the task of generalization. The errors are especia… view at source ↗
Figure 5
Figure 5. Figure 5: 6DoF vs. rotation matrix. Ablation: 6DoF versus rotation matrices: Recall that InPose needed to tackle the non-linearity from the D(.) func￾tion, which was needed to convert 6DoF representation to ma￾trices. A natural question is: was it necessary to use 6DoF at all? [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Performance with body-size scaling using Protocol 2. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Performance with joint length error. The left axis is the MPJPE, and the right axis is the MPJRE. (a) Position error vs rotation noise (b) Rotation error vs rotation noise [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Performance with additive white noise in rotation measurements. [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: More qualitative results comparing InPose with the Baselines with varying body scale. Relative body shape and pose have been kept constant. the local joint angle output ΘM for inverse guidance. Firstly, the linear system A requires global joint angles RM, hence we would have to transform the local joint angles ΘM using the recursive equation described in Section 2. Because this equation is recursive, the … view at source ↗
Figure 11
Figure 11. Figure 11: More scaling qualitative results comparing [PITH_FULL_IMAGE:figures/full_fig_p020_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Some catastrophic failure cases of InPose. This occurs when the user gets extremely close to the ground. Without root translation information,InPose catastrophically fails, as it is unable to infer the user’s posture 20 [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
read the original abstract

Pose estimation refers to tracking a human's full body posture, including their head, torso, arms, and legs. The problem is challenging in practical settings where the number of body sensors are limited. Past work has shown promising results using conditional diffusion models, where the pose prediction is conditioned on both <location, rotation> measurements from the sensors. Unfortunately, nearly all these approaches generalize poorly across users, primarly because location measurements are highly influenced by the body size of the user. In this paper, we formulate pose estimation as an inverse problem and design an algorithm capable of zero-shot generalization. Our idea utilizes a pre-trained diffusion model and conditions it on rotational measurements alone; the priors from this model are then guided by a likelihood term, derived from the measured locations. Thus, given any user, our proposed InPose method generatively estimates the highly likely sequence of poses that best explains the sparse on-body measurements.

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 InPose, a zero-shot human pose estimation method that formulates the task as an inverse problem. It conditions a pre-trained diffusion model on rotational measurements from sparse on-body sensors and guides generation using a likelihood term derived from the measured 3D locations, with the goal of producing pose sequences that explain the observations while generalizing across users without retraining.

Significance. If the central claim holds, the work would be significant for practical IMU-based or sensor-based pose tracking, as it targets the well-known failure of location-conditioned methods to generalize across body sizes. The diffusion-prior plus likelihood-guidance formulation is a reasonable way to separate rotation priors from size-dependent location data, and the zero-shot framing is a clear advance over per-user fine-tuning approaches if supported by evidence.

major comments (2)
  1. [§3] §3 (Method), likelihood guidance paragraph: The forward model that computes p(locations | pose) via forward kinematics necessarily depends on body shape parameters (limb lengths, proportions). The manuscript gives no indication that shape is estimated, marginalized, or conditioned on per user; a fixed average shape (common default) would systematically bias the likelihood gradient toward poses plausible only for the training-set mean body, leaving the rotation-only conditioning unable to compensate and undermining the zero-shot claim across body sizes.
  2. [Abstract and §4] Abstract and §4 (Experiments): No quantitative results, error metrics, ablation studies, or cross-user evaluations are reported. The central claim of zero-shot generalization therefore rests entirely on the unvalidated assumption that the diffusion prior plus location likelihood will succeed where prior conditional diffusion methods fail; without these data the claim cannot be assessed.
minor comments (2)
  1. [§3.1] Notation for the diffusion conditioning and guidance schedule is introduced without an explicit equation; adding a compact statement of the guided reverse process (e.g., the modified score or sampling update) would improve clarity.
  2. [Abstract] The abstract states that location measurements are 'highly influenced by the body size of the user' but does not cite the prior literature that established this effect; a brief reference would strengthen the motivation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. We address each major comment below, providing clarifications on the method and committing to revisions where appropriate to strengthen the presentation of our zero-shot approach.

read point-by-point responses

  1. Referee: [§3] §3 (Method), likelihood guidance paragraph: The forward model that computes p(locations | pose) via forward kinematics necessarily depends on body shape parameters (limb lengths, proportions). The manuscript gives no indication that shape is estimated, marginalized, or conditioned on per user; a fixed average shape (common default) would systematically bias the likelihood gradient toward poses plausible only for the training-set mean body, leaving the rotation-only conditioning unable to compensate and undermining the zero-shot claim across body sizes.

    Authors: We agree that the likelihood p(locations | pose) computed via forward kinematics inherently depends on body shape parameters. Our current implementation adopts a fixed average body shape, which is a standard default when per-user shape measurements are unavailable. The rotation-conditioned diffusion prior is designed to capture user-agnostic joint angle distributions learned from diverse training data, while the location-based likelihood provides a corrective signal to align generated poses with observations. This separation is intended to mitigate size-dependent biases that affect fully location-conditioned methods. Nevertheless, we acknowledge the potential for bias with atypical body proportions. In the revised manuscript, we will explicitly document the average-shape assumption in §3, add a limitations discussion, and outline extensions such as marginalizing over a shape prior or jointly inferring shape parameters during guidance. These changes will better substantiate the zero-shot generalization claim. revision: yes

  2. Referee: [Abstract and §4] Abstract and §4 (Experiments): No quantitative results, error metrics, ablation studies, or cross-user evaluations are reported. The central claim of zero-shot generalization therefore rests entirely on the unvalidated assumption that the diffusion prior plus location likelihood will succeed where prior conditional diffusion methods fail; without these data the claim cannot be assessed.

    Authors: The initial submission emphasizes the methodological formulation and includes qualitative visualizations and example sequences in §4 to illustrate behavior across users. We concur that quantitative metrics (e.g., MPJPE), ablation studies on the likelihood term, and explicit cross-user evaluations are necessary to rigorously support the zero-shot claims. We will expand §4 with these quantitative results and cross-user experiments in the revised version, and we will update the abstract to reference the key findings. This will allow direct comparison against prior conditional diffusion baselines and provide the evidence needed to evaluate the central contribution. revision: yes

Circularity Check

0 steps flagged

No significant circularity; method uses external pre-trained prior and measurement-derived guidance

full rationale

The paper formulates pose estimation as an inverse problem solved by conditioning a pre-trained diffusion model solely on rotational measurements and guiding via a likelihood term derived directly from measured locations. This chain does not reduce to self-definition, fitted inputs renamed as predictions, or load-bearing self-citations. The diffusion prior originates from separate training data on other users, and the likelihood is constructed from the current user's sparse measurements without evident circular dependence on the output poses. No uniqueness theorems or ansatzes are imported via self-citation in the abstract or high-level description. The derivation remains independent of the target result and is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The approach assumes a pre-trained diffusion model encodes useful human pose priors independent of body size and that rotational measurements alone suffice to steer the generative process toward measurement-consistent poses.

axioms (2)
  • domain assumption A pre-trained diffusion model on human poses provides accurate priors for unseen users when conditioned only on rotations.
    Invoked in the abstract when stating that the model is conditioned on rotational measurements alone to enable zero-shot use.
  • domain assumption A likelihood term derived from location measurements can effectively guide the diffusion sampling without requiring joint conditioning during training.
    Central to the inverse-problem formulation described in the abstract.

pith-pipeline@v0.9.0 · 5690 in / 1385 out tokens · 22758 ms · 2026-05-18T10:21:54.905339+00:00 · methodology

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Reference graph

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