Zero-shot Human Pose Estimation using Diffusion-based Inverse solvers
Authors: Sahil Bhandary Karnoor
Romit Roy Choudhury
Venue: ICLR 2026 Poster (2026)
Keywords: Human Motion tracking
Inverse problems
Wearable sensors
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: Our diffusion based algorithm performs 3-point pose estimation without needing any specific fine-tuning on a new user's body size through an inverse-guidance likelihood score.

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 is 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, primarily 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.

Published as a conference paper at ICLR 2026
ZERO-SHOT HUMAN POSE ESTIMATION USING DIFFUSION-BASH INVERSE SOLVERS
Sahil Bhandary Karnoor & Romit Roy Choudhury University of Illinois at Urbana- Chammaainn Champaign, II. 61820, USA (sahilb5, croy) @1 mellinod nois .ddu
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 is limited. Past work has shown promising results using conditional diffusion models, where the pose prediction is conditioned on both (location, rotation) mcasurements from the sensors. Unfortunately, nearly all these approaches generalize poorly across users, primarily because location measurements are highly influenced by the body shape of the user. In this paper, we formulate pose estimation 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,
inverse problem and design algorithm capable
INTRODUCTION
Human pose estimation is a crucial piece for numerous applications, including medical rehabilitation, virtual and augmented reality (AR/VR), sports coaching, health monitoring, and performing arts. An ecosystem of pose-estimation tools already exists. In lab settings Stathopoulos et al. (2024), the
environment is instrumented with visual/infrared cameras to track user pose so long as they are in the camera's s field of view. In un- instrumennted settings, such as homes or offices, virtual reality (VR)
technologies aim to achieve full-body pose tracking using VR goggles and two handheld controllers Han et a (2020). The results have steadily improved Dittadi et al. (2021) ) Pavlakos et al. (2019)
with recent boost from generative models (e.g., conditional diffusion models Castillo et (003))
that predicted the user's full-body pose from just 3 sensors. Unfortunately, such proposed generative techniques have a significant limitation; they don't I generalize well across users with varying body shapes. A generative model trained data from single user can't 't be used by user with a different body shape without fine-tuning Authors in Alliakbariam et al. (2022) try to overcome this issue by jointly training over both pose datasets and varying body shapes, but this increases model complexity, and there is no guarantee that all possible body shapes were accounted for during training. An algorithm that generalizes even body shape outliers would be ideal.
In this paper, we propose InPose, diffusion-bassd method that implicitly accounts for the user's body shape without requiring any fine-tuning. Our core observation is that any human's full-body pose can be decomposed into a "scale-free pose" and "scale-dependent" component. For human poses, the scale-free pose can be imagined as template human body whose skeletal joints (c.g.. shoulders, elbows, hip, knees, etc.) are rotated appropriately to create given pose. The scale- dependent component is the joint locations in 3D space. Forward kinematics relates the scale-free pose, along with the body shape, to the scale-dependent component. Since the sensors give (rotation, location) measurements from 3 body joints, it is possible to estimate a distribution of scale-free poses from rotational measurements alone. Then, the location measurements can be used to sharpen this distribution to poses that best explain the measurements. This decomposition lends itself to an inverse problem formulation, shown visually in Fig. [I. Using (rotation, location) measurements from 3 body necessary to fully define the 3D pose of human.
head and two wrists mrists-InPose amms to track the locations of all 22 body joints,

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Published as a conference paper at ICLR 2026
W
1-3
1-2
4i clbow
InPose
4
bj
4: joint with multiple children
4: root
Joint angle
Transiation
Figure 1: (a) InPose's input and output visualized over 4 time frames. (b) "T" pose. (c) Pose with depiction of the rolation angle and root translation.
InPose's 's inverse problem formulation can be sketched as follows. We train a Diffusion model conditioned rotational measurements from existing datasets; this gives conditional prior over scale-free poses. When inferring a specific user's ' pose, we use the user's 's body shape to scale the scale-free pose, and compare it against location measurements estimate the likelihood of the pose. This likelihood term requires propagating a Gaussian random variable through a nonlinear operator. We prove that this propagation can be approximated by a Gaussian and use the likelihood as an inverse kinematics guidance term to guide the diffusion denoising process. The denoised result is sequence of full- full-body pases-samples from the posterior that best explains the 3-point measurements for that specific user. Through extensive experiments, we demonstrate promising generalization across wide range of body shapes and shapes on the AMASS dataset Mahmood et al. (2019).
2 MODEL AND MEASUREMENT
Body Model: Following the conventional SMPL framework Loper et al. (2015). we model the human body as a graph (Fig. [T). The vertices of this graph are the 22 main joints in the human skeleton; the edges are the bones connecting these joints. The 3D coordinates of the joints (in a global reference frame) are denoted as 1 E RR E (1, ..2). The bone that connects adjacent joints I 4 are denoted by vector bj.A R of fixed length 16yu]. Every joint L7 has a unique parent, 4.. The whole joint-tree has root roittoint / located at the pelvis.
The globul pose of body is fully defined by the 22 joint locations in global reference frame. Fig D shows "T" pose and le shows a running pose. Intuitively. global pose can be computed in three steps. (1) Start with a standard "T" pose with the human located the origin of global reference frame. (2) Move the root joint 2 to bring the human to its correct location; the human is still in the "T" pose, but the whole body is displaced. (3) Now, starting from joint 4,, rotate each joint based on the joint angles. Perform this sequentially down the joint tree, ensuring parent joint p; has been rotated before rotating joint j. These 3 steps bring us to the human's global pose.
Eq. I models the above steps to compute joint j's global location at time frame i.
(1) 1p, (i) + Rp, (i) bjus
Here Ray (i) is global 3D rotation matrix of the parent joint. Note that the global 3D rotation matrix for any joint j is computed as (1) Rp (i) (i), where (1) is the local 3D rotation matrix shown in Fig IC. Since θ,(1) (1) is represented as 3D rotation matrices all the joint angles in the "T" pose identity matrices. As the human performs different poses, InPose aims to track the root location 7 (i) and global rotation (i) for each of the joints.
Joint Angle Representation: While representing (i) using 3D rotation matrices makes it easy to compute joint locations, Zhou et al. (019) has shown that representing them instead using the
Other representations are possihle, including axis-angle, Fuler, quaternion representation.
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6D parameterization (1) R6 is better for neural network training. This is due to the continuity properties of this representation, unlike others such as quaternions or axis-angle mapping for vector (i) is computed as:
The forward
ry(1) = [R R
(1)
R (1)
R (
R
(D)
where (1) is the {k: 1h element of the corresponding 3D rotation matrix. There also exists non-linear differentiable inverse D 120 + I 3x3 that maps the 6D representation to rotation matrices
(defined in Appendix AA. Hence, Eq I becomes: (i) = 4p, (i) + p(rp, (i)) .bjp,. We extend D to map all MM| rotations Trom 6D to rotation matrices, and term this function D.
Measurements: To align with recent work in this area Zheng et al. 2023); Castillo et al. (002)), we use the same AMASS dataset, which contains locations and rotation angles of the head and two wrists. These measurements can be obtained from VR goggles and handheld controllers, which use a combination of ego-centric cameras, IMU sensors, visual SLAM algorithms, and dead reckoning methods estimate locations and rotations in the global reference frame, where m C M- = [1,...22] and |m| 3.
INPOSE: INVERSE ZERO-SHOT POSE ESTIMATION
3.1
FORMULATION
We denote the noisy signal measurements from the 3 sensor joints as Vn (1) = indexes the measurement time frames and is dropped throughout the rest of the paper unless specified. 1. (i) = 1 (i) + orv(i) is the noisy location measurement, where + is the noise-free joint location, and (i) is iid Gaussian noise. Similarly, the rotation F' (i) is also noisy. Our goal is to predict тM- which are the 22 global joint rotations and 4,, the root's translation. We are also provided the user's bone lengths bj.p.- With such sparse measurements, this an ill-posed pose estimation problem.
Here i
As in Jiang et al. (2022), we simplify this problem by first assuming the root joint is stationary. We estimate the scale-free pose defined by rMG then scale to the correct pose defined by las; and then drag this pose until the head's location matches the measured head location, hhedd From this, we infer the root translation 4, Thus, the core question boils down to sampling from the posterior p(rM p(rM|)))) (Bm)
Diffusion models have recently found remarkable success for these types of posterior sampling problems. They were originally proposed as a tool for sampling from a prior distribution po (x").
This is done by first defining a noising process (x') by injecting iid Gaussian noise of standard deviation oN into it, where t E {0 T'. Diffusion models aim to reverse this noising process by learning the score function V V2, log (((e)).
In our scenario, we require the conditional score V log Pe(r's (rm)).) One method is to use Classifier- Free Guidance (CFG) proposed by Ho & Salimans (202T). In this formulation, a conditional diffusion model is trained accept all the inputs you. Imy T'm] for conditioning. Most previous works Castillo et al. (2023); Van Wouwe et al. (024)) use this approach and are unable to support zero-shot generalization. This is because the (noisy) location measurements Im vary based on body shape- two people are in the same pose, their joints would share identical rotation angles, but because of differences bone lengths across varying body shapes, we see from Eq. that the joint locations 1; will be different. Thus, a conditional model trained on one user's s data does not generalize well to another.
To overcome this, we split the conditional score V,g, log Polr's (ym) V log pe Pe Hm }m) using Bayes' rule:
V log (r (m,, {mm) V log (r (rm) + Vrg, log Pe (Im (mmlrrrr (r rm) +
(2)
V,; log Pr (rm) + Vrg, log pt pr(mmrr where we have assumed I,,, and I'm are conditionally independent.
(3)
The conditional score V,,, v log pe(rfy (rm) is scale-free, and can be learned by CFG-based condi- tional diffusion model. The scale-dependent likelihood score V log pe (e (rM) can be utilized as a
Rotation matrices may also he used, hut since they must be made unitary, output pose quality is affected.
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guidance to the prior Chung et al. (0023); Kawar et al. (0022) Rozet al. (024)) Yang et al. 2024).
This guidance is performed during inference and does not require any training or fine-tunit of the generative neural network. We use the Pseudoinverse- -uriiiinnerseeGGiinneee for Diffusion Models (IIGDM) Song et al. 2023) framework, but propose a mechanism to propagate a Gaussian random variable through the non-linear inverse (.) function inside the likelihood term (discussed soon). This mathematically enables the decomposition of the user's s pose into general scale-free pose (from the prior) and a user- specific scaling factor (captured in the likelihood). Thus, our main contribution over past work-and the key to enabling zero-shot pose- pass-preictioon is to perform CFG using only the measured rotations, and use only the joint locations pseudoinverse guidance that CFG.
3.2 DESIGNING THE LIKELIHOOD, PRIOR, AND POSTERIOR TERMS
We now describe the various score terms used by our algorithm at each diffusion timestep.
CFG Prior: We train a CFG-based score model eg (rAs. t, rm)) to determine the conditional score Vri log pe (r is [mm) as function of the noisy rotation inputs I'm from the 3-point sensors. This is then used to derive conditionally denoised estimate Fh using Tweedie's formula Efron (011))
The VI (ree (as,, bar,,,r 1,,m)
M
Vā
transformer architecture used in BoDiffusion Castillo jet al. (2023) but modify it appropriately (details in Section 4 since we are only conditioning on rotation T Fim while BoDiffusion conditioned on both I' and 4m-
We adapt the same DiT Peebles & Xie 2022
Likelihood score: To compute the likelihood score Vri, log pe(Im/rkn we need to relate the joint rotations to joint locations. Mathematically, we aim to minimize the likelihood 114m A D lll where (.)) is the meusurement operator. Recall, D(.) converts 22 joint rotations from the 6D vectors to rotation matrices, and A is linear function that uses these rotation matrices determine the joint location estimates. Eq. I had earlier shown the operation of A for a single joint, where (1) (i))
Unfortunately, two issues stand the way of estimating the likelihood. Since r'ss is noisy estimate of TMr, we cannot pass it through the measurement operator to obtain Im To mitigate this, we adopt ideas from IIGDM Song et al. (2023) to help approximate r&, as a Gaussian distribution. non-linear function, propagating the approximated Gaussian random variable through D(.) is problematic. We will prove that this propagation can also be approximated by Gaussian, giving us a pathway to the final solution. Let us briefly review 1IGDM first and then visit
Since D.)
the second step in InPose.
Consider the general problem where we are given observations = Ax + agn, where the measurement model, n is unit Gaussian noise, and og is the noise variance. Say we would like to estimate zº, for which we will guide diffusion model that is denoising a noisy x' at each diffusion time step. This guidance needs to use the likelihood score V,, log (=(2="), which needs to be computed through an intermediate step of marginalization over x as follows:
I1GDM Recap:
pe(2|=²) = f
(5)
If A is linear function and the noise n is Gaussian, then p(z[=") ~ ((xx" (AA,",,)) For the second term p=<x)2²)) 1IGDM proposes to approximate this distribution as Ni w(l), where the mean comes from a regular diffusion step. Hence, the distribution pt (2|2*) and the corresponding likelihood score can both be approximated by Gaussian follows:
Pe(z|=') se NAA wfAA +
(6)
A Oi Ox T
log (2(x)')) = ((2- A (W) AA + -1
(7)
Let us now return to InPose. We cannot directly apply IIGDM to our likelihood score V log (r(mll)y)) since our measurement operator contains the D(.) function. But if D(.) is
that the rotation matrix R2 is somehow available then the measurement opera-
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3-point measurements
m
Classther fiee Guidanee 6o(
M
Fr
Eakilhood
FM
(0. - A---V)))
-01
aD( Oh
InPose pipeline: 3-point sensor rotation location measurements are inputs. Rotations are fed to the CFG score model. which outputs conditional prior; location measurements estimate the likelihood, which used steer diffusion.
Figure 2:
tor in Eq. becomes linear. When 1 0, the joint location 1j becomes a matrix-vector product, Ck, by traversing the joint tree as follows:
[R....pp)) Rp,] [1 10.1....b. J., =
(8)
where [R... Rp, and A [63] 10.0,..0pppp IT. The matrix-vector product can be rearranged to j.ps
form (Ig & vec(C) C) where & is the Kronecker product. We can thus obtain linear function A = Ia 2 K Plugging this A into Eq 7 gives us the likelihood score.
Unfortunately, the D(.) function is non-linear in InPose, hence the conditional distribution (=(2(=*) in ii no longer Gaussian. However, using the following Theorem, we show that it is possible to approximate pem4mm/rr (.4mmr'' Ir's)) Gaussian and compute its covariance matrix with well-trained score model eg (proof in Appendix A).
Theorem We are given well-trained score model co, that learns the score function 6
co(rhesh L Fm), and denoises fM +- Vi--vi- If the model ensures that 11/5.110 -115.3
Vol
1. (p1.1s . p2,30) 0, Vj E M then p. (r ze N(D(Fts)) wp where E is a positive definite matrix.
From the proof of Theorem Using this approximation, get Pr (P((2 == (D(((P))))) w Epp )r and thus
we obtain the covariance matrix for D(F as Cov(D(()))) = D
Vr,, log log Pe(lllr'rr) ((hm A (w A + of (D(F%))
Or
3.3 ACCOUNTING FOR TRANSLATION: DIFFERENTIAL PARAMETERIZATION
From Eq. [ we see that all joint locations at frame have an additive dependence on 1 (i) due to the kinematic chain:
(1()) =
(p (i) Rpu (1) Di.p.) + Rí (1) b2.1 + (1)
(10)
3==3
The mapping A derived from Eq. 8 is only valid if (1) 0.
To enable the linear inverse guidance formulation when (1) * O, we use the difference between the positional measurements from each of the 3 measured joints at every 7t frame. Thus, the contribution of the root translation 4 (i) for each the measured joint locations gets canceled. However, lower body motion estimation remains limited because there is direct translational guidance. InPose relies heavily the prior learned by the diffusion model to estimate lower body motion.
For body shapes similar to the default shape,
the head translation input to the CFG model, enabling us to estimate lower body motion. We perform experiments both with and without head translation as CFG-based input.
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3.4 MODEL PIPELINE
Figure summarizes the InPose pipeline. Its objective can be summarized as performing guided diffusion to infer a sequence of human poses rM, using a combination of conditioning CFG inputs and Pseudoinverse guidance utilizing modified IIGDM likelihood score. The inputs to the algorithm are the noisy joint rotations rjεm and locations Ijem subset of joints of size 3.
At each diffusion step t, InPose's workflow can be summarized in the following steps:
Use the CFG score function Vri, log Pt (Tm) conditioned on noisy rotation inputs {rm} (Eg) generate conditionally denoised estimate T'M-
Use LIGDM to estimate the likelihood score Vrj log Pe Pe Mr))
Combine the conditionally denoised estimate and the likelihood score using modified DDIM generate the diffusion output for the next step T'M" The proposed InPose algorithm described in Algorithm
Algorithm InPose Inference using modified ITGDM
Require: N, co. E [0, 1], A. D()) Inputs: Ym = [m f'
Find sequence of timesteps N with go 0 and 9N T Initialize FM ~ 10,
for i [N..1] do
t + 9:
$ f 9:-1
D Get start and end times Get for VP-SDE
Ito?
x co (rM, L, m'm)
Vuee PM Vā
Denoised output at current iteration
C1 t //
Bi 1-aa
Constants for DDIM
au
2 - /I - Us of
wi + Cov(f(s)
g (Im - A. -(Fff)))) (wRAE,, AT + +of1)-1
@D(F Orir
Likelihood score
Sample ~ ()0,1)
I'M Va + Cit C2Ft + Vang
Posterior update
end for return FM
Return Estimated Pose sequence
pertinent question one may ask as follows. Given that the user's body shape parameters are available during inference, why not scale the default-body dataset with these body shape parameters? Said differently, applying Eq. [ the scale-free joint rotations TM can be scaled-using the available body shape parameters- to regenerate locations (M. This new dataset can then be used to train CFG model, obviating the need for inverse solvers like InPose. Unfortunately. this is possible only if 1 known (or equal to 0). Otherwise, the mapping between l'M to I non-trivial. As illustration, consider that the user jumps. Without modeling the dynamics of the human body, is hard determine the user's displacement while airborne. This is why pure CFG models are unable to generalize across body shapes, while InPose's inverse guidance formulation does not require new datasets from new users.
4 EXPERIMENTS
Datasets: All our experiments were performed on AMASS Mahmood et al. (019), an aggregate of multiple human-pose datasets and the de facto standard for pose estimation/ estimation/generationn today. The data is in the SMPL body model format. Each dataset within AMASS consists of multiple samples, each a sequence of poses at 60, 100, 120Hz: resample all data to 60Hz. A fixed default body shape typical of the average male is used for model training. Our experiments follow two dataset protocols, per BoDifuusion baseline Castillo al. (0023):

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Published as a conference paper at ICLR 2026
1. The Transitions Mahmood et al. (2019) and the HumanE VA Sigal al. (2009) datasets within AMASS were used for testing. while others were used for training.
An approximately 90%/10% split for training and testing respectively, on the CMU Carnegie
Mellon University, BMLrub (rojje (0022). and the HDM05 Müller et al. (2077) datasets.
Baselines: We choose the two following SOTA algorithms as baselines. These models accept the 3-point joint locations im (t), rotations I' (i), and the corresponding velocity and angular velocity as inputs; they output the full-body pose the user for which each was trained. Both the velocity and the angular velocity are computed as linear functions of Im and mmm The rotation and angular velocities are represented in the 6D representation.
AvatarJLM Zheng et al. 2023 is conventional neural sework-bbsse approach.
BoDiffusion Castillo et al. (2023) is the CFG-based diffusion model that we adopt for InPose. In the original BoDiffusion paper, it outputs the local joint angles A, (i). The authors provide
pretrained model, denoted BoDiffusion(Loca in all our results.
BoDiffusion(Global) We modified MoDiffusion(Local to output global joint angles r'M- This also evaluated using both In and I'm for CFG and is termed BoDiffusion(Globbl)
Implementation Details:
We fine-tune the neural network used BoDiffusion and perform inference using N 50 steps. The original BoDiffusion algorithm implements DiT Peebles & Xie (2022)
based denoiser a CFG score model framework. Additional details are provided in Appendix
Evaluation Metries: We use 4 standard metrics from the literature to evaluate the models:
Mean Per Joint Position (location) Error(MPJPE)) measures the mean joint location error, in cm, across all joints and poses the sequence.
Mean Per Joint Rotation Error(MPJRE) measures the mean joint rotation error, in degrees, across all joints and poses. MPJPE captures the scale-dependent error, while MPJRE captures the scale-free error.
We also report the joint position error, in cm, for the upper and lower body separately, respectively. These two tell us how well the model can infer upper body versus leg movement, given that all measurement sensors are placed on the upper body.
UPE, LPE:
4.1 RESULTS
Zero-shot generalization across body shape scaling: Fig. Ba.b) presents results when the models are trained on default body shape, and then tested for various body shape scaling factors (including the default). body shapes are varied by changing the scaling factor on the X axis (a value greater than 1.0 on the X axis indicates proportionally taller human, and vice versa). Note, all bones of the taller person (or shorter) human have been scaled up (or down) by the same factor (we also report separate results where different parts of the body are scaled differently). The root joint translation is also proportional to this scaling factor. The results are performed using Protocol 1. We report location and rotation errors (MPJPE and MPJRE). Importantly, we divide the estimated MPJPE by the scaling factor; The MPJRE is obviously scale-free.
The baselines outperform InPose in the default case when scale equals 1.0. However, both the scaled MPJPE and the MPJRE (Fig. 33 3p) remain almost flat for InPose regardless of body shape. This demonstrates the zero-shot nature of our inverse solver, in contrast the significant degradation observed in the baselines.
Robustness 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 default body shape and the rotation measure- ments. This is an important test for practical applications since real-world wearable sensors-lis watches and phones have difficulty with measurement errors. Fig. 3 shows the location error under increasing Gaussian noise variance (the rotation error is reported in the Appendix). Evidently, InPose stays flat while other baselines degrade with noise. This is expected because while BoDif- fusion's 's model is sensitive to location noise, InPose uses the location only for inverse guidance,
We provide the code on our wehsite httpp:://cctipppsseeeeeeeee for reproducability.
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17.5
40
Rxchmnitatt AvstadLMM
(cm)
(dognees)
AvstalLM chest
Chas
MPJPU (cm)
10.0
20
10
5.0
0.6
0.8
1.0
1.2
Scale
Scale
Location Noise
Deviation (cm)
Figure (a) Position error vs. body shape scaling. (b) Rotation enror vs. body scale. (c) Position error vs.
location noise. All these tests were performed using Protocol 1.
allowing the prior to play an important role in the final pose estimates. In our experiments, we also found that the velocity error in noisy conditions is lower in the of InPose than with BoDiffusion. The output pose sequences from the baselines have high jitter, indicating the estimated poses out of distribution.
Qualitative results with scaling: Fig presents qualitative comparisons between InPose and BoDiffusion(Globa)) for the default body shape 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 the task of generalization. The errors are especially prominent in the 0.6 case, where BoDiffusion predicts the lower body be in squatted pose because the measurements are generated by user of short stature. Since the priors were learned by BoDiffusion from data generated by user with the default body shape, there is way to inform the model of this difference. For the 1.4 case too, BoDiffusion incurs higher torso error.
Ground Truth BoDiff(Global)
InPose(Ours)
Default Scaling
High error
60% Scaling
140% Scaling
Low
Varying relative sizes of body parts: Table reports results when the body parts are not scaled up or down uniformly; instead, limbs and torso are scaled with different scaling factors. This accounts even outliers human variations, e.g., basketball players with longer arms, or athletes with longer legs. To create the ground truth data, we scale bone lengths first, which are then used to recompute the joint locations |ys from the scale-free joint rotations TM Unfortunately. the root translation is non-trivial function of TM and the body shape. But in general, observe that 4 is similar across body shapes that share the same lower body bone lengths. Thus, we preserve both and the lower body shape, and only vary the upper body shape for these tests.
Figure 4: Qualitative results with scaling body shape. The same pose is used for all scales.
Evident from Table [ (and more results in Table i the Appendix), the results are aligned with previous graphs. With the default body shape, the baselines outperform InPose on all 4 metrics. This because the respective neural networks are able to learn a complex non-linear mapping from both the joint rotation
and the location inputs L,, to the user's pose since the training body shape (default shape) and the inference body shape are identical. In contrast, InPose uses linear constraints using l.,. to steer the output towards an estimate of the pose sequence that best explains the input.
However, when bone lengths change, the baselines are misguided by the input locations and therefore do not generalize. InPose outperforms both baselines across the MPJRE, MPJPE, and UPE metrics, inverse location guidance accounts for changes in body shape. Unfortunately, the LPE error remains higher for InPose ose in some cases.
Using Head translation for CFG: We also test using the head joint position input the CFG score model. This is termed InPose (head) in Table 2 The shape variations compared are less extreme than in Table to be more favorable to the Baselines.
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Table 1: Algorithm comparison for varying upper body shape. The metrics used are Mean Joint Position Error(MPJPE) in em, Mean Joint Rotation Error(MPPRRE) in degrees, Upper Joint Position Error(UPE)) em, and Lower Joint Position Error(LPE) in em. The lower body shape was kept the same, while the upper body bone lengths were scaled.
(a) Results with Upper hody shape variation (Protocol 1) (4 is better)
Algorithm
Default shape MPJRE
Upper body MPJRE
x1.4
Arms x1.4,
Torso x0.7
MPIPE
MPIPF
UPF
MPIPE
MPJRE
Avatar]1
4.92
4.25
2.13
9.94
26.09
7.02
25.46
27.47
18.89
9.33
14.76
25.95
BeDiffusion(Loaa)
5.16
4.32
2.36
9.72
25.69
15.35
22.79
30.21
9.98
9.24
8.05
13.33
(iDiifusion(Cloobl)
5.97
4.97
2.35
11.96
13.40
11,48
10.91
17.93
7.61
7.24
5.15
11.98
InPoae
7.64
6.38
3.36
14.74
9.15
6.71
4.80
16.31
7.45
6.52
3.23
14.6
(b) Results with Upper body shape variation (Protocol 2) (4 is better)
Algorithm
Default shape MPJRE
Upper body
x1.4 UPE
Arms x1-4.
Torso x0.7
MPIPE
UPE
MPIPE
MPJRE
LPE
МPIРЕ
MPIRE
UPE
LPE
Avatad]MM
3.54
3.11
27.13
8.36
26.02
29.21
20.32
9.34
15.83
27.98
BeDitfusion[ ocal)
3.59
2.68
1.51
7.0
26. 12
14.51
21.34
33.62
10.13
8.41
8.13
13.63
BoDiffusion
4,90
17.39
12.71
13.59
23.77
7.25
14.85
InPose
7.53
4.73
2.9
15.04
8.94
4.73
3.97
16.85
6.96
4.77
2.60
14.17
In this comparison, find that although the MPJRE of InPose without head input stays consistent across body shapes, falls behind the other baselines when tested with relatively minor variation in bone lengths compared to the default body shape. However, by simply adding the head position CFG input to the diffusion model, InPose (head) outperforms all baselines across the tested body shape variations in nearly metrics. Furthermore, we see considerable improvement in lower body motion estimation as evidenced by the LPE metrics.
Table 2: Algorithm comparison for varying upper body shape using Protocol 1. InP (head) augments InP ose by using the head translation input for CFG. The Shape variation this table is less extreme than in Table
Algorithm
Upper body x0.85
Upper body x
Arms x0.85,
MPJPE
MPJRE
UPE
LPE
MPJPE
MPIRE
UPE
LPE
MPJPE
MPJRE
UPE
LPE
BeDifiusiom(Locn Local)
7.44
7.47
5.03
11.57
11.72
8.76
9.21
15.82
6.27
4.91
3.64
10.58
NoDiftusion(Globall
6.76
7.79
3,59
12.16
8.18
6.99
3.57
12.66
5.7
3.36
12.49
InP
7.13
6.26
2.87
14.25
8.25
6,51
3.95
15.37
7.8
6.33
3.46
14.95
TnFose ose (head)
6.08
5.06
2.4
12.06
5,92
4.51
2.63
11.29
5.71
4.5
2.37
11.16
Ablation: 6D versus rotation matrices: Recall that InPose needed to tackle the non-linearity from the (-) function, which was needed to convert 6D) representation to matrices. natural question is: was it necessary use 6D at all? Figure 5 shows the importance of 6D over 3 x 3 rotation matrices. We train two unconditional UNET-based diffusion models-one using the 6D representation and the other using 3D rotation matrices. These models generate 64-frame human pose samples as global joint angles in their respective representations. These poses are then used generate sequences of body meshes. The raw rotation matrix meshes (labeled RoT-Raw) from the Rotation matrix UNET exhibit high jitter. In contrast, the 6D meshes(labeled 6D) from the 6D UNET have much lower jitter. This is primarily because the output rotation matrices not unitary. In fact, postprocessing the rotation matrices using (.) on the first 2 columns (labeled RoT-Post) considerably lowers the jitter.
30
Titter
20
10
RoT-Raw RoT-Post 6DoF Figure 5: 6D vs. rotation matrix representation for Diffusion compared using Jitter (} is better).
More qualitative results are shown in the Appendix. and we provide some animations on our website: kttps://icc ri npose- wrypto. www.snocopoo.oooommmmm
o TCLR ICC.R/

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Published as a conference paper at ICLR 2026
5 RELATED WORK
Deep-learning based methods for pose-tracking: Deep learning has achieved significant success in estimating pose from sparse set of measurements, (Aliakbarian et al. (2023); Du et al. (2023); Yuan et al. (202) all use HMD-based location and rotation sensors to estimate pose and translation.
Nearly all these works focus using sensor information from the head and the two wrists. [ ct al. (2022) estimate pose by first using a Transformer encoder to estimate local joint angles, and then estimates translation by fitting the generated head translation the head location sensor input. Castillo et al. (2023) use a CFG diffusion- based approach to estimate pose. Most of the above- specifically trained for single user's body parameters. which comes at the cost of worse generalizability. One work that does generalize across users. [Aliabbarian et al. (2022) jointly trains sensor inputs and bone length parameters in flow-based generative model framework. But this algorithm requires jointly training pose and a large number of body shapes in order to generalize. In contrast, our work can directly accept any set of body bone parameters without requiring any bone shape generalization training.
mentioned approaches
large number of works |Huang et al. (2018); Mollyn et al. (2023) focus on pose and translation prediction using sparse set of IMUs that provide acceleration and orientation data from the joints to which they are attached. Yi et al. (2021) use a cascaded sequence of Neural networks to predict pose from 6 IMU sensors. Yi et al. (2022) incorporate physics-based dynamics constraints on the user's s joint motion to improve pose estimation accuracy.
Human Motion synthesis: A closely related topic to pose estimation is pose synthesis Raab et al. (2023); Tevet et al. (2025). This usually involves training a generative model on a human motion dataset, along with textual labels, generate motion either based on prompt unconditionally. Shafir et al. (2024) use a trained motion synthesis model as a prior for more complex tasks, such as motion blending and multi-person interactive motion.
Some of these textual models also accept other inputs to serve as guidance for the generated motion Tessler al. (024); ; Diller & Dai (2024) Xie et al. (2024) generate human motion from textual prompts using a diffusion model, but can also use a gradient-descent deseint--ddseent-bbeedded inverse guidance method to specify motion trajectories of various joints. Their work is closely related ours, but they also require a munnformer-bbseed realism-guidane guidance module that encodes joint location-control signals.
6 LIMITATIONS AND FUTURE WORK
The biggest limitation of InPose is that the root translation isn't directly incorporated into the algorithm, which reduces its capability to infer lower body pose. Thus, the algorithm can only use the prior to infer translation and, thereby, infer lower body movement. This limitation can be addressed by using the head position input the CFG score model as InPose (Head). This is, unfortunately, at the cost of reducing the inherent flexibility of our inverse guidance approach.
Another major limitation is that InPose cannot outperform the basclines in the default body shape scenario. A reason for this may be that the Gaussian approximation in Theorem I may not be sound.
Furthermore, InPose requires the user's body bone lengths during inference. An interesting direction to pursue is blindly determining the user's bone lengths without an explicit calibration procedure.
7 CONCLUSIONS
We propose InPose, a diffusion-based model that estimates the user's 3D fully-body pose sequence from 3 sensor measurements. By decomposing poses into a scale-free and a scale-dependent component, we find a pathway to an inverse problem formulation that turn enables zero-shor generalization. As a result, any new body shape or shape need not be re- re-trained with personalized data; InPose is able to guide the diffusion-based prior by computing whether the samples from the prior are consistent with the joint location sensor measurements. There is room for improvement at least on two fronts, namely in outperforming the baselines for the default sizes for which they are trained, and in improving lower body errors by better modeling the physics of leg movements. We believe these are rich problems for future research.
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8 ACKNOWLEDGEMENTS
We thank the anonymous reviewers for their invaluable feedback. This work was partially supported by NSF #2008338, #1 #1909568, #2148583, and #MRI-2018966. This work used DELTA at NCSA through allocation CIS230230 from the Advanced Cyberinfrastruccure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by U.S. National Science Foundation grants #2138259, #21 38286, #2138307, #2137603, and #2138296.
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A PROOF OF THEOREM
Theorem We are given a well-trained score model co, that learns the score function 62 <-
yoa co(r 1m), and denoises i'M fa--i-ases If the model ensures that ||,5)1-011 ||7,3%011
(f)) , = O, Vj E M then pe (D(r))))) NDD(FM)) wf where Epe, a positive definite matrix.
Proof. (Sketch)Wa We will prove the validity of the Gaussian approximation for single joint rotation rj. This result can then be naturally extended to ris because each joint rotation is independent the global reference frame. From IIGDM, we approximate pe Pr(r (r3) Re N w²t). This implies that 0..k every element " vk {1 6}, Vj E M N wy) are ii.d. Gaussian Random variables.
The mapping D(r)) R2 is defined as:
c²
d (t)
||c|||
2 [1000]]
-proja ([r401)"))
c
c²
||
e dt e²
(t) [ct 22 (
(11)
By the definition of D(r)) = n.. the unit norm constraints 1|:4:1:311 || = 1, and (p5.33 : 34,300) = O, the elements of the first two columns, (2..(12.1:33) (( w are also Gaussian random variables that are uncorrelated. Now, the elements of the third column 0,3:6 of each R8.13.1:3) are the result of the cross-product 0:1: x r Let's first discuss (².(3.2) " (ry3, ,y. ,...y.)) The mean of this random variable is:
Eir
0.3 0,4
53 5
0,1 0.6
0.3 0.4 B[rr
[5 [6[ 0.1 0
(12)
(13)
(14)
Thus, all the elements of the third column 12, (3,11:3
product terms. Next, compute the variance have their mean given by the respective cross-
0.3 0,3_0,4 0,4 Varly Tj rj r 18(0
011 1
0,4
0.1
0.6
)
Eir,
0.3 0,4
rj 5j 7j
0.1 0.6,2
(15)
Treating the terms separately, we get
0.3 0,4 00,1 0..1_...... 0.6 BI(r [j rj ";
= rj -00000000--111,04440--0000000034
(16)
(17)
= (w? + +- 2.3 Fj
(18)
2wx + (P5 ++))
(163 12.12 2 2. 2t
(19)
using the independence property and the definition of variance. Next,
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Err 0.4
0.1
[E]r
13
(20)
=
= r )2 + (1)' ,,
(21)
Substituting the above terms into Eqn. 15 get
Varlr) 0.3 0.4
99 -rj" 19:17 r w (2w} (66)))2 +
(f)
(22)
Finally, we compute each the covariances of the third column elements R2. 13.13.1:3) To compute ( u.1).
Cov(r 0.3
3 0.11
Covlr 0.3 ,,
j .T [ov (2
0,4 00.1
(23)
E(rg.0,, [j {j
0,4 0.11
-
",
(24)
= -ww + (3 + (25
(25)
(26)
and Cov[RR R. (3.2))
Cov(r) 0.2.00 0,6
0.3 0,5 0.3
0,3,0.4
0.1 0.6
- - j ;
-r "
= =0- - Covlr,
0.3 0.5
u.3 0.44
Covlr 1,, .r 1
0.6
0.1 0.6 0.6
(27)
=E[r 0.5 1|r 9
0.3
0.3
(1( (,.0,))),.005111 0.4 Eir 0.2 0.6
B 9
0.1
0.2
(28)
0
1 + 200--(rj
u pt.2
(29)
- + +
2t.1 p1.
(30)
The list of variances is the following:
Var Var(R (3..1111) w2( + + + (1/,522 (3)
Var( (3..2) w2w2 + (1 (1) (1/ ()2)
Var R) (3..3)
w2w + 2+ (15.5)2 (F/ ( (17)
and the covariances are:
Cov(R
0,2 (3.11)
r3. u
RRv 00.1 - w 7
Cov(R (33.3)
0.1 = w pt.b
Cov(RO
(3.11)
0,3
- w 7
Co .j 1.. = w w
Cov(R j
0.(3.) 9.
0.2 = w 24.4
[j
Cov(n2
(3,11)
0,5
-wet t
2t.3
Cov(ne (3,22) 0,41 w wppp.
Cov(R 2
03333)
= - w 2/.
5
3
Cov|R (3.1)
0.6
Cov[R 0.3.2) 0,6 w 7
Cov(Rt 0.3.3) 2.
0.51 = w p/
r
Cov(R] R2. (3.2) = ( + sgAggs)
Cov(R].(11) R. (3.3)
= - 265260
Cov(RR (3,3) 12 13.13.1) -w +1 (AAN00
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while the terms that have been omitted are all 0.
Now that we have the respective variances and covariances, we can build the positive definite covariance matrix for pa (D(r)) vec(R))lr)), which at diffusion step t is Eg.. To show that Eg positive definite matrix, we use Sylvesters criterion Gilbert (199T. It states that a symmetric matrix Σ E IR N is positive definite if each upper left corner matrix of sizes n E fl..... N) has positive determinant. Since En a relatively large matrix of size x 9, we use SymPy Meurer et al. (2017) to compute each corner matrix determinant.
We find that each determinant equals positive number that depends on w The first corner matrices trivially have determinant 1 since they are all identity matrices. The determinant for n = {7.8.9) (termed "nx") are the following:
det(E = 2w²
det(I = (2w,)²
det(Exg)) = (2w?)²
thus proving that Mig is positive definite.
Since we assume every rotation in r'M independent of each other, when we combine the w Egg for each rotation, get large positive definite matrix wf E Thus, approximate the distribution Pa (Drr (rrs)) as a Gaussian using the mean and the derived positive definite matrix.
B IMPLEMENTATION DETAILS
As stated earlier, we use BoDiffusion as the base Diffusion model for InPose. The conditional inputs for the CFG score model are the joint rotations, locations, the joint velocities, and the angular velocities from the 3 measured joints. The rotations output of the network is the 22 joint rotations Oи in the local reference frame, expressed in 6D. The model is designed to output frame of 41 samples. For sequences larger than 41 samples, it uses overlap of 20 samples between successive frames.
provided using the 6D representation. The
Since require the model output joint angles r'r in the global frame, fine-tune it training datasets to produce global rotation angles. We use the weights provided by the BoDifuusion authors to initialise fine-tuning. We also tune the CFG model to only joint rotations and angular velocities by training it on a subset of samples with zeroed-out joint locations and velocities. During seteeee-uudanne-baaseee inference, we can similarly zero out the location and velocity CFG inputs to the diffusion network.
This model has about 22M parameters. Finetuning done using the DDPM framework for N 1000 steps. Inference is done for N = 50 steps using DDIM, for both pure CFG-based guidance as well as InPose's inverse guidance. We used an Nvidia RTX Titan GPU for days of training with a batch size of 256.
Inverse Guidance: For the IGDM--based inverse guidance term v,, po rig) we used an additional scale parameter, which we found was useful in improving performance for all metrics. Increasing this term Secondly, since the positive definite matrix Σ,,, is very large, its usage substantially slows down run-time. We found that setting Eep, to simply the identity matrix performs reasonably well.
minor increases MPJPE performance at the cost of worse MPJRE error.
C MORE RESULTS
Performance with varying upper body(Part2): We show more results with varying upper body shape in Table We once again see that InPose is able to generalize better to changes relative bone lengths, but lags behind the other baselines in lower body pose estimation.
Performance with scaling body shape on Protocol 2: Figures ba and 6b are the performance results from scaling body shape using Protocol 2. The tests conducted in manner similar to what
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Table 3: Algorithm comparison for varying upper body shape. The metrics used Mcan Joint Position Error(MPJPE) in em, Mean Joint Rotation Error(MPPRRE) in degrees, Upper Joint Position Error(UPE) em, and Lower Joint Position Error(LPE) in em. The lower body shape was kept the same, while the upper body bone lengths were scaled.
(a) Results with Upper body shape variation (Protocol 1) (4 is better)
Upper body
x0.7
Arms x x1.4
Arms x0.7
Algorithm
MPIPE
MPJRE
UPE
LPE
MPJPE
MPIRE
UPE
LPE
MPJPE
MPJRE
UPE
LPE
A
20.59
10.33
19.02
23.32
9,21
7.21
7.79
11.84
7,90
8.15
5.88
11.46
BoDiffusion/Loaal
9.00
11.29
6.68
13.2
15.57
11,99
13.32
19.29
9.36
8.84
7.12
13.27
BoDiffusion
7.44
10.56
4.35
12.81
9,51
9.02
7.18
13.66
4,89
12.88
InPose
6.67
6.17
2.42
13.80
8.25
6.67
3.97
15.4
7.22
6.20
2.92
14.35
(b) Results with Upper body shape variation (Protocol 2) (L is better)
Algorithm
Upper body x0.7
Arms x1.4 MPIRE
Arms x0.7
MPIPE
MPJRE
UPE
LPE
МPЛРЕ
UPE
LPE
MPTРЕ
MPJRE
UPE
LPE
Avatad]M
21.63
9.06
19.86
24.56
10.30
8.02
8.57
13.36
7.44
6.67
6.17
9.65
BoDiffusion(Local
1.72
6.06
10.67
15.93
11.11
12.77
21.11
6.95
6.30
9.91
BoDiifusion(Glebbul
9.17
9,32
6.72
13.38
13.19
10.76
9.83
18.94
9.19
7.17
7.13
12.63
InPose
13.84
Boal
BoDut)ilobal
15.0
Sealed MPJPE (cm)
Ar
BeDilltecal
BeDiffGGohhl)
20
Our
MPRRE (degrees)
12.5
Avateel M Ours
15-
10.0
5.0
1.0
1.2
1.4
2.5
Scale
0.6
0.8
1.0
1.2
1.4
Scale
(a) MPJPE divided by Scale, default body
shape scaling
(b) MPJRE body scale
Figure 6: Performance with body-size scaling using Protocol 2.
is described in Section 4.1 We once again that InPose performs worse than the baselines in the default case, where the same body shape used during training is also used for testing. However, when the default body shape scaled, InPose outperforms the baselines. MPJRE, which measures scale-free performance, remains constant, while the scale-dependent MPJPE scales proportionally with scaling.
Robustness to measurement noise(entd): Here are the rotation eттоr results from the robustness study we performed in the Section 4.1 As stated earlier, we injected zero-mean i.i.d. Gaussian noise into the input location streams and computed the estimation errors, while maintaining the default body shape. Fig. 7 shows the rotation (MPoo(MPPRRE) under increasing Gaussian noise variance the location measurements. As with the location error, I InPose stays flat while other baselines degrade with noise. Since the IIGDM inverse guidance objective formulated be robust to noise, prior is able to synthesize poses that are realistic while also obeying the guidance provided by the location inputs.
Errors in joint length estimates: Another study involves illustrating InPose' 's sensitivity to errors in the joint length estimates, where we show how the MPJPE and MPJRE worsen when there an error in knowledge of the user's bone lengths. We add white Gaussian noise to the true bone lengths while constructing our measurement matrix A. The bone length parameters are set to the default body shape, and the measurements Vm unaltered.
Figure & shows the results for this experiment. We see that InPose is quite sensitive to bone length estimation error. The algorithm tolerate low emors within em, but begins diverge any higher
17

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Published as a conference paper at ICLR 2026
20
LoDil))
ReDifGGlohal)
14
MPRRE
MPRRE (dogrees)
A
MPIRI: (degrees))
1.5
Ours
MPPE (cm)
10
6
6
10
5
10
15
Joint Noise Standard Deviation (cm)
Location Noise Standard Deviation (cm)
Figure 8: Performance with joint length error. The left axis the MPJPE, and the right axis is the MPJRE.
Figure 7: Rotation error vs location noise.
8
BoDillLocdl)
LoDi))
(lDoil(
Gobibi
MPIRE (degrees)
8
MPJPE (cm)
10
15
10
15
Rotation Noise Stundard Deviation (degrees)
Rotation Noise Standard Deviation (degrees) (b) Rotation error vs rolation noise
(a) Position error vs rotation noise
Figure 9: Performance with additive white noise in rotation measurements.
than that. Making the algorithm robust to bone length estimation errors will be looked at for future work.
Robustness to rotation noise: We conduct another robustness study with rotation measurements, similar to the location error study described in Section 4.1 Here, we add Gaussian noise to the rotation measurements while keeping the location measurements noise-free. We then compare the InPose with the two diffusion-based baselines. The results are summarized in Figure p We find that all the diffusion-bart based algorithms relatively robust to rotation measurement noise, with both the rotation and position errors rising steadily as rotation noise increases.
Need for using location measurements: An important question is whether purely using the scale- free rotations rm is better than including the scale-dependent location measurements I,, Since r'm is scale-fres could potentially
only rm to estimate pose. As ablation study, compare (oDiffusionnGlobbl) and InPose, against BoDiffusion BoDiffusion(Gllbal) was trained accept only I'm both {rm. {m) for CFG-based conditioning, this serves as an apples-to-apples comparison.
without using Im for CFG. Since
Table shows the results of this comparison using Protocol using the default body shape generate bn- BoDiffusion(Glor
with 4n for CFG performs the best amongst the three algorithms. InPose ose next, performing much better than BoDiffussonnGlobbl) without Lm, illustrating the importance of 2. for pose estimation.
Table Comparison between InPose, BoDiffusiio((lloba) and BoDiiftssio(Gllbba) with 4m
as input for CFG using Protocol
Metric
InPose
BoDiffusionn(lobbl)
BoDifftusion(llobal) no Im
MPJPE(cm)
7.64
5.97
15.98
MPJRE(")
6.38
4.97
8.71
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Published as a conference paper at ICLR 2026
Using Inverse-Guidanee Local Joint Angle Representation: Considering that the BoDiffu- sion(Local) model outperforms the BoDiffusion(Globall model, an important question w-hyy not the local joint angle output 2a for inverse guidance? In earlier comparisons, we saw that Local prediction achieves better lower-hody (LPE) performance.
To compare Inverse Guidance performance for Local joint angle prediction with Global joint angle prediction, we fine-tune BoDiffusion(Local in a manner similar to InPose (head) as described in B such that it accepts head position along with all 3 rotation angles input for the CFG score, but the hand positions are zeroed out. We use a IIGDM-based inverse guidance after computing the global joint angles using the local angle output from the score model, which we term Local(IIGDM), We also use an L2-based gradient descent method similar to that in Xie et al. (2024) to perform inverse guidance using joint location. We term this method I (GradeGtadient Descent) and Gradint Descent) based on whether the score model predicts local or global joint angles, respectively.
Table 5: Comparison with Inverse Guidance for Local Joint Angle Prediction using Protocol
Algorithm
MPJPF(cm)
MPJRE(()
UPF(cm)
LPE(cm)
Local(Gradient Descent)
5,51
5.25
3.32
9.47
LoCal(TGGMM)
5.82
5.16
2.48
11.27
Gradaetrr Descent)
5.58
4.97
2.71
10.14
InPose ose (head)
5.85
4.69
2.47
11.34
The results are summarized in Table The LoadieGra Descent) is once again better than InPose and its variants at predicting lower body motion, resulting in lower MPJPE. However, this the cost worse upper body performance and higher overall joint angle error. InPose, which uses ITGDM was specifically chosen in our work for zero-shot pose prediction because of better upper body inverse guidance, especially when head position isn't 't used for the CFG score model.
Runtime Performance: We next test the runtime of each algorithm in Table 6 reporting the number of samples estimated per second for each, Each algorithm was tested on an RTX Titan. Given that we operate at 60 frames per second across all baselines, all algorithms run in real time, although some latency is expected.
Table 6: Runtime performance comparison between InPose, BoDiffusion and Avatar]I.M
Algorithm
Runtime (samples /sec)
AvatarJLM
~ 102
BoDiffusion
~ 392
InPose ~ 229
More Qualitative Results: We show another qualitative sample in Figures 10 and I1 comparing InPose with the baseline algorithms in running pose. Once again, we keep the same relative body shape and modify only the scale. inPose falls behind the baselines when it comes to estimating lower body pose, especially the feet. However, it generalizes across all body scales tested, with error at the arms lower than that of the baseline algorithms.
Unfortunately, InPose without head position as CFG input catastrophically fails in some scenarios when predicting lower body motion, as seen in Figure [2] But this is mitigated by using the head position as input to the CFG score model, as InPose (head).
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Published as a conference paper at ICLR 2026
Ground Truth
AvatarJLM
BoDiff(Local)
BoDif((Global)
InPose
Default Scaling
High error
60%
Scaling
Scaling
Low error
Figure 10: More qualitative results comparing InPose with the Baselines with varying body scale. Relative body shape and pose have been kept constant.
Ground Truth
BoDiffLLocal)
BoDiff(Global)
InPose
Default Scaling
60% Scaling
140% Scaling
Figure 11: More scaling qualitative results comparing InPose with the Diffusion- -aseed Baselines with varying body scale. InPose is able to infer lower body movement using the prior learnt from hand motion during walking.
Ground Truth
BoDif(Local)
BoDil((lobal)
InPose
InPose(head)
Failure infer lower body motion
Figure 12: Catastrophic failure cases of InPose. This occurs when the user gets extremely close to the ground. Without root translation information, InPose fails to infer lower body motion. This is alleviated by using the head position input for CFG, in InPose (head).
20


Reviews:
[{"review_id": "oRPU9Z8Nxg", "reviewer": "Reviewer_zAJP", "summary": "This paper introduces InPose, a diffusion-based method for human pose estimation from sparse sensor data (3 sensors: head and two wrists). The key innovation is formulating pose estimation as an inverse problem, enabling zero-shot generalization across users with different body sizes—without requiring model fine-tuning for each user. InPose leverages a pre-trained diffusion model conditioned on rotational measurements, using location measurements only as a likelihood-based guidance term during inference. The method is evaluated on the AMASS dataset and compared to state-of-the-art baselines, showing strong generalization and robustness to body size and measurement noise.", "questions": "Please address the concerns in the weakness session.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ Novel Inverse Problem Formulation: The decomposition of human pose into scale-free (rotational) and scale-dependent (location) components is elegant and addresses the generalization issue in prior work.\n+ Zero-Shot Generalization: InPose can handle unseen body sizes and shapes without retraining, a significant practical advantage.\n+ Robustness: The method is robust to measurement noise, as shown in experiments with noisy sensor data.\n+ The paper provides mathematical justification for the Gaussian approximation in the likelihood term and details the propagation of uncertainty through non-linear operators.\n+ Evaluations cover generalization to new body sizes, robustness to noise, and ablation studies on representation choices (6DoF vs. rotation matrices).", "weaknesses": "- Performance on Default Body Size: For users matching the training body size, baseline methods outperform InPose.\n- Complexity: The method involves non-trivial mathematical machinery (e.g., modified ΠGDM, covariance propagation), which may hinder adoption. Please include complexity analysis.\n- The proposed method may be hard to be used in online real-time applications, where most of the real usages cases in VR and AR require.\n- The proposed method builds on top of several previous methods, reused and simplified some formulations. Please clearly define the novelty over these previous methods.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5cltU04nM6", "reviewer": "Reviewer_tqDn", "summary": "This paper proposes an approach for estimating the 3D human pose from sparse body-mounted sensor measurements (e.g., using three sensors that track position and orientation). The method employs a diffusion-based framework, with the key contribution being the advantage of accommodating different body shapes, unlike prior methods that assume a fixed body shape. The approach is evaluated on the AMASS dataset and compared against previous work.", "questions": "As I describe in the weaknesses, I would be interested in seeing:\n- experiments with more realistic scale variations (e.g., with body shapes from AMASS).\n- a comparison (conceptual, and potentially experimental) with the EgoAllo design.\n- a version of the baselines where they see data with different scales during training (i.e., augmentations on the body shape).", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- The proposed method achieves improved robustness compared to previous work, specifically BoDiffusion and AvatarJLM.", "weaknesses": "- The evaluation is limited to synthesized data (for noise and different scales).\n- The experiment using Gaussian noise is interesting, but this is not necessarily representative of the type of noise that these sensors tend to have.\n- The experiments varying the body size introduce some arbitrary scaling of the upper body or the arms/legs. Is an 1.4x scaling for the torso while doing a 0.7x scaling for the arms realistic? Why not sample real motions and real bodies from AMASS for these experiments?\n- I understand that the baselines are trained with a specific body shape, but how would they perform if their training data is augmented with arbitrary scaling factors for the different body parts. Is scale robustness something that can be achieved with augmentations?\n- Although the setting is different, there has been recent work that operates with arbitrary body shape parameters (EgoAllo, Yi et al, CVPR 2025). How does that compare to the proposed work?\n- Minor: The paper refers to the representation of Zhou et al as 6DoF. My understanding is that this representation still has three degrees of freedom, they just regress 6 values, so they refer to it as 6D.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kodFE0eZ2T", "reviewer": "Reviewer_wWuf", "summary": "This paper proposes a new diffusion-based human pose estimation method for sensor data. The core motivation is that human skeletons have diverse limb lengths, so a conditional diffusion model fails to be trained on that diverse data. To bypass this problem, the paper proposes to train the diffusion model only on rotational data, which is independent of limb lengths. To realize this idea, the conditional diffusion formulation is separated into two parts, i.e., rotational distribution and locational distribution. The rotational distribution is absorbed in a classifier-free guidance (CFG) formulation and trained on data, while the location part is simply formulated based on geometry. To handle the location part in reverse steps, the pseudo-inverse guidance technique is utilized. This technique requires the transform to be linear, which is not satisfied by the geometric property of the rotations, so the result of the technique is approximated by a Gaussian distribution. Experiments show that the proposed method is much better generalized for diverse data.", "questions": "Please see the above weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- Good design. I'm impressed by the separated CFG design. This cleverly bypasses the difficulty of limb-length differences.\n\n- Good generalization performance.", "weaknesses": "- Some crude components/approximations: The approximation for the $\\Pi$GDM is somewhat crude. (i) It requires a strong assumption. The assumption in Theorem 1 requires the 6 DoF representation to be already close to a Stiefel matrix. However, this may not be true for a large part of the diffusion process. (ii) Theorem 1 is somewhat misleading. The proof only shows that the mean and the covariance of the distribution will correspond to the derived expressions. This does not imply that the resulting distribution will be close enough to a Gaussian one. It can be a widely different distribution, which still has the same mean and covariance.\n\n- Better generalization, but worse in the best-case scenario? In Figure 3, InPose is worse than existing methods when the scale is one. The paper says that this is \"expected,\" but why is this? I cannot find a valid reason for the proposed formation being worse with the default scale. A more thorough discussion is needed here.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "EYV0exyqv3", "reviewer": "Reviewer_ny6y", "summary": "The paper proposes InPose, a diffusion-based inverse solver for human pose estimation from sparse sensors. The model assumes access to rotations and positions of only three joints (head and two wrists). It decomposes poses into scale-free and scale-dependent components and uses a pre-trained diffusion to restore full-body poses with sparse observations. During inference, it adopts ideas from IIGDM to incorporate positional likelihood, allowing zero-shot adaptation to new body scales without fine-tuning.", "questions": "1. Could the authors clarify how r_m could be physically obtained from only three sensors? Are these intended as device orientations or joint rotations, or just dataset artifacts?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 3, "contribution": 2, "strengths": "1. The paper introduces a diffusion-based inverse inference framework for human pose estimation, which is conceptually original. It demonstrates how diffusion models for forward generation can be adapted to approximate inverse inference by adding an explicit likelihood-guidance term during sampling. \n2. The decomposition of human poses into scale-free and scale-dependent components is theoretically motivated. \n3. Within the paper’s stated assumptions, the derivation is internally consistent. Although these assumptions are physically unrealistic, the formulation itself is mathematically correct.", "weaknesses": "1. The entire formulation assumes that the three sensors directly provide joint rotations r_m. In practice, such rotations cannot be measured without knowing both adjacent body segments’ orientations. This input variable is thus non-observable, and the model seems to operate on dataset-level ground-truth artifacts rather than physically measurable signals. \n2. According to Fig.1, the conditioning joints (head and wrists) are leaves in the kinematic tree. While leaf-joint rotations may be defined as parameters in datasets, they are not constraining in the forward-kinematic chain (i.e., not required to determine downstream positions). Conditioning p(r_M|r_m) on such non-constraining variables makes the setup physically non-closed: the learned dependence is statistical rather than geometrically consistent, weakening the realism and deployability of the approach. \n3. The paper argues that zero-shot generalization is needed because users have different body scales, yet it simultaneously assumes that the new user’s bone lengths are known at inference time. If scale information is already available, a simpler and physically grounded approach would train a scale-free diffusion prior and condition on the user’s scale-dependent skeleton (e.g. scaled T-pose) together with observable head/wrists measurements (if available and physically meaningful). Such a design would achieve the same finetune-free behavior without introducing complicated conditioning mechanisms.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)