KinemaDiff: Towards Diffusion for Coherent and Physically Plausible Human Motion Prediction
Authors: Ye Lu
Jie Wang
Tianyi Liu
Jianjun Gao
Kim-Hui Yap
Venue: ICLR 2026 Poster (2026)
Keywords: Human Motion Prediction
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Stochastic Human Motion Prediction (HMP) has become an essential task for the realm of computer vision, for its capacity to anticipate accurate and diverse future human trajectories. Current diffusion-based techniques typically enforce skeletal consistency by encoding structural priors into network architectures. Although effective in promoting plausible kinematics, this approach provides only indirect control over the generative process and often fails to guarantee strict physical constraint satisfaction. In this work, we propose a structure-aligned and joint-aware diffusion framework that enforces physical constraints by embedding skeletal topology and joint-specific dynamics directly into the diffusion process. Specifically, our framework consists of two key modules, the Joint-Adaptive Noise Generator and the Structure-Aligned Constraint Enforcer. The former component, Joint-Adaptive Noise Generator, infers joint-specific dynamics and injects
heterogeneous, instance-aware noise per joint and sample to capture spatial variability and enhance motion diversity. The latter component, Structure-Aligned Constraint Enforcer, encodes skeletal topology by modeling joint connectivity and bone lengths from historical motions, and it constrains each denoising step to preserve anatomical consistency. Through their synergistic operation, these modules grant KinemaDiff direct control over physical realism and motion diversity, addressing the common limitations of indirect structural priors and uniform noise application. Extensive experiments on multiple benchmarks demonstrate the effectiveness of our method, attributable to tailoring the diffusion process through structural alignment and joint-adaptive noise modeling.

Published as a conference paper at ICLR 2026
KINEMADIFF: TOWARDS DIFFUSION FOR COHERENT AND PHYSICALLY PLAUSIBLE HUMAN MOTION PRE- DICTION
Ye :u';Je Jie W Jianjun Gao', Kim-Hui Yap' Nanyang Technological University 2Beijing Institute of Techholooy
ABSTRACT
Stochastic Human Motion Prediction (HMP) has become an essential task for the realm of computer vision, for its capacity to anticipate accurate and diverse future human trajectories. Current diffusion-ban based techniques typically enforce skeletal consistency by encoding structural priors into network architectures. Al- though effective in promoting plausible kinematics, this approach provides only indirect control over the generative process and often fails to guarantee strict physical constraint satisfaction. In this work, we propose a structure-aligned and joint-aware diffusion framework that enforces physical constraints by embedding skeletal topology and joint-specific dynamics directly into the diffusion process. Specifically, our framework consists of two key modules, the Soint-AAaptivv Noise Generator and the AtructureAAlgnen Regularizer. The former component, Joint- Adaptive Noise Generator, infers joint-specific dynamics and injects heteroge- neous, instance- noise per joint and sample to capture spatial variability and enhance motion diversity. The latter component, Structure-Aligned Regularizer, encodes skeletal topology by modeling joint connectivity and bone lengths from historical motions, and it constrains each denoising step to preserve anatomical consistency. Through their synergistic operation, these modules grant KinemaD- iff direct control over physical realism and motion diversity, addressing the com- mon limitations of indirect structural priors and uniform noise application. Ex- tensive experiments on multiple benchmarks demonstrate the effectiveness of our method, attributable tailoring the diffusion process through structural alignment and joint-adaptive noise modeling.
INTRODUCTION
Human Motion Prediction (HMP) (Barsoum et al., 2018) aims to forecast future human motion se- quences based on past observations, which is crucial for applications like autonomous driving (Paden et al., 2016), assistive robotics (Gui et al., 2018), and virtual avatars. While early deterministic meth- ods (Xu al., 2023; Li et al., 2022; Ma et al., 2022) sought (a) Current: take the same noise distribution to predict a single most likely future, they fell short in capturing the inherent unpredictability of human actions. Therefore, accurately modeling the multimodal and phys- ically plausible distribution of future motions emerges as
a paramount yet daunting task. To address this challenge,
(b) Ours: take joint-specific noise distribution
Joint
stochastic methods have gained prominence, with denois-
ing diffusion probabilistic models becoming the main-
stream approach (Yuan & Kitani, 2020; Dang et al., 2022). Joint
These models demonstrate remarkable capabilities in gen- erating diverse motion sequences by progressively refining random noise into coherent human poses, as Fig. 1.
Joint
Figure 1: Comparisons about the noise
taken between other basclines and ours. Despite their generative power, these diffusion-basee methods face two critical technical limitations in producing coherent and physically realistic human poses throughout the iterative diffusion pro-
"Equal contribution.

---

Published as a conference paper at ICLR 2026
Time
Noisy Motion
Observation
Observation
Target Motion
Generative Diffusion Model
Structure Extractor
Sampling
Align
Noise Generator
Joint-specific
Observed Structure
Noise Distribution
Predicted Motion
Feature
Figure 2: (a) Illustration of joint-adaptive noise generator. We propose a joint-adaptive noise deter- mined by both the joint characteristics and the human motion observations. These noises are then added to the corresponding human motions to be predicted. (b) Representation of cructure-Aligned Regularizer, which identifies the human structural information from the historical motion and lever- ages the extracted structure to guide the motion generation during the diffusion process.
On the hand, uniform noise schedule is typically applied across all human joints, failing to account for their heterogeneous motion patterns. Different joints exhibit vastly different degrees of freedom and dynamic behaviors. Applying identical noise profiles overlooks these unique kine- matic properties, resulting in disordered or physically invalid predictions that compromise motion diversity and realism. On the other hand, prior methods tend neglect the human skeleton's 's in- herent anatomical structure. They often rely on implicitly learning the structural constraints (Chen al., 2023; Sun & Chowdhary, 2024) or post-procesd post-proeessing corrections (Wei al., 2023), without integrating these constraints into the diffusion process, which leads to the generation of physically implausible poses with stretched or compressed bones, undermining motion realism, as in Fig. 2.
To address the aforementioned limitations, we present KinemaDiff, novel kinematics aware dif- fusion framework that fundamentally reshapes the denoising diffusion process, via explicitly em- bedding anatomical consistency and kinematic heterogeneity. Our framework consists of two core modules: the Joint-Adaptive Noise Generator and the Prructure-Aligneee Regularizer, as illustrated in Fig. 2. The first component, the Joint-Adaptive Noise Generator, is responsible for capturing and injecting heterogeneous motion patterns. It learns and applies instance-specific, heterogeneous noise profiles to different joints, effectively adapting the noise characteristics based their unique dynamics and varied degrees of freedom, thereby guiding the diffusion process to generate dynam- ically rich and realistic motions. Subsequently, the ArructureAAlined Regularizer, is tasked with rigorously enforcing anatomical consistency throughout the generative process. It achieves this by directly integrating bone length constraints into the denoising procedure, leveraging stable struc- tural features extracted from historical motion observations to ensure that generated poses adhere strictly to human biomechanies. Through the synergistic operation of these two modules, Kinemal)- iff enables direct and explicit control over physical realism and motion diversity, moving beyond the limitations indirect structural priors and uniform noise application.
We extensively validate the effectiveness of our proposed diffusion model on Human3.6N, and more challenging cross-dataset scenarios on AMASS. Our model outperformed previous models with multiple evaluation metries on these datasets. Our contributions be summarized follows:
We introduce KinemaDiff, novel diffusion framework that integrates human skeletal structure and joint-specific motion dynamics directly within the diffusion process.
We propose learnable coint-adaptive noise generator to enhance motion diversity and novel structural alignment mechanism to enforce anatomical consistency.
We validate the effectiveness of our method through comprehensive experiments on Hu- man3.6M and the cross-dataset AMASS benchmark.
2 RELATED WORK
Stochastic Human Motion Prediction. Early research in Human Motion Prediction (HMP) fo- cused on deterministic forecasting using sequential models like RNNs and GCNs (Jain et al.,
2

---

Published as a conference paper at ICLR 2026
2016; Dang et al., 2021). To capture the inherent multimodality of human actions, this field has shifted from deterministic forecasting to stochastic generation. Initial generative approaches, pri- marily Variational Autoencoders (VAEs) (Walker et al., 2017) and Generative Adversarial Networks (GANs) (Barsoum et al., 2), pioneered the generation of diverse futures but often struggled with long-term coherence and physical plausibility. STARS (Xu al., 2022) further advances multi- modal motion modeling by introducing deterministic spatio-temporal anchors that enable diverse yet controllable future predictions. Recently, Denoising Diffusion Models (DMs) have become the dominant paradigm, offering superior fidelity and diversity. Research in this area has seen rapid progress, from pioneering the paradigm with a two-stage framework Motiondiff (Wei et al., 2023), simplifying the training pipeline via the masked completion model Human (Chen et al., 2023), to introducing latent diffusion for more coherent, behavior-driven sampling BuLFousoo (Bar- quero et al., 2023) and incorporating specialized architectures such as GCN-DCT to better capture spatio-temporal dynamics in CoMusion (Sun & Chowdhary, 2024). More recently, SLD (Xu et al., 2024) constructs semantically structured latent motion space through Semantic Latent Directions, enabling precise and interpretable control over generated motions.
A concurrent line of work has also begun to adapt the diffusion process itself, such Skeleton- Diffusion (Curreli et al., 2025), which introduces anisotropic noise based on the skeleton's 's static structure. In contrast to these approaches, which largely treat the core diffusion mechanism fixed component or adapt it based on static priors, our work is the first to fundamentally reshape the denoising process be dynamically aware of human kinematics. We achieve this by introducing a novel framework equipped with Structure-Alined Regularizer for anatomical consistency and a learnable, instance-adaptive Joint-Adaptive Noise Generator, offering a more flexible and physically grounded generative process.
Denoising Diffusion Probabilistic Models. Denoising Diffusion Probabilistic Models (DDPMs) (Ho et al., 2020; Nichol & Dhariwal, 2021; Song et al., 2020) have emerged as a pow- erful class of generative models, capable of synthesizing high-fidelity data by learning to reverse a progressive noising process. While these models have been successfully applied to a wide range of human-centric tasks (Gong et al., 2023; Shan et al., 2023), such applications have predominantly fo- cused on innovating the denoiser's 's network architecture, while largely adopting standard, generic diffusion process. However, a recent and promising research direction has begun to demonstrate significant gains by tailoring the diffusion process itself, particularly through task- task-specific noise designs (Sahoo et al., 2024; Huang et al., 2024). Following this trajectory. we introduce a novel dif- fusion framework specifically for human motion, which moves beyond architectural modifications. We propose to fundamentally reshape the denoising process with a joint-adaptive, structure- mechanism that inherently adapted the kinematic properties of human skeleton data.
3 METHODOLOGY
3.1 PROBLEM FORMULATION
As illustrated in Fig. 3, we denote the observed motion history of H frames as x(1:H) x();,, (1) E "x33,, where ((k) RR represents the joint coordinates at frame h, and J is the total number of joints. Given x(1:1) the goal of Human Motion Prediction is to fore- cast the subsequent F frames (1:F)) ((H+1HH+F) [x(H+1); x(H+)) x (H+F)) E Fx33,
where y(f) Jx3, and J is the number of body joints.
3.2 PRELIMINARIES
Motion Diffusion. Let {w denote a Markov noising process, where Yo represents the true data samples. For training, we progressively corrupt the target human motion sequence {ys If by adding noise. This forward diffusion process is represented as:
g(yt 11-1) = M(n Vou Vt-1, (1 (1-a)) a;)
(1)
where {a E [0, 1] controls the noise level. To approximate the underlying data distribution, the reverse diffusion process is formulated to iteratively remove noise from the corrupted samples yt, starting from f. = T down to t = 1, as follows:
po(y1-1 v+) NV NN (1 Mo 1), oz ()T)
3

---

Published as a conference paper at ICLR 2026
x03
2t13
Daba
Nuise
Laliign
9
Inkidd Ensoddrr
pred
Joint-adapiie Distibution
Lalign
Strusture-alineed Loss
Predicsed Motion
$6
Naisy Motion with
Noise
Frequency Components
Clean Puture
Noise Funue Mition
Extracted Bone Length Dute preel Observed and Predicted Motion
Muxius
Trequecy specific Graph
Figure 3: The overview of our proposed Kinemadiff. (a): Our Joint-adaptive noise generator. We learn joint-adaptive noise from the historical human joints and add it to the future human motions. (b): Initial motion reconstruction. The future human motion with injected noise is processed through a self-attention mechanism, which generates an initial prediction in the absence of external condi- tioning. (c): Structure-
Regularizer. The initial prediction is concatenated with the motion observations and then processed the frequency domain through frequency-awarc GCN, and sub- sequently transformed back to the temporal domain for structural alignment.
In addition, following prior work (Chen et al., 2023; Barquero et al., 2023), we condition the model on the historical motion. The conditional reverse diffusion transition then formulated as:
po(y1-11 ve, (Bc-1: x, oz
where æ denotes the motion history and ye the noisy motion step
Direct prediction. In existing diffusion models (Chen et al., 2023; Barquero et al., 2023), the objective typically either to predict noise or to directly predict human motion. this work, we adopt the latter, as it enables more effective optimization of the diffusion process through the incorporation of skeletal structural priors.
3.3 OVERALL ARCHITECTURE AND NETWORK DESIGN
Overall Architecture. As illustrated in Fig. 3, our network takes the observed motion history (1:H) E [RH33J and the future frames y yl:FF) E Rx33J as input. The future frames are perturbed with our joint-adaptive noise, which varies across joints and samples, yielding sequence {ye ) that follows the forward diffusion process. Our model is trained to reverse this process and ultimately reconstruct the predicted future motion ýo E I8 Fx33 First, we feed the noisy future frames y, into an encoder composed of several Transformer layers to obtain an initial reconstruction Yo E R F3J To ensure structural alignment, we reshape the motion history as (1:H) E IR x and the noisy future Yo E F Xx3 Next, concatenate the initial reconstruction with the historical motion and process it with the Alignment Module to produce the denoised prediction.
Network Structure. As illustrated in Fig. 3, our network architecture consists of three main compo- nents: a joint-adaptive noise generator, an initial encoder, and a Structure-Aligme Regularizer. The first component generates joint-adaptive noise conditioned on fixed-length motion history and joint characteristics using few linear layers. The second component employs temporal Transformer encoder to generate an initial reconstruction, providing baseline prediction without conditioning. The third component is the proposed Pructure-Aligned Regularizer, which models the full mo- tion sequence in the frequency domain via DCT/IDCT. Unlike prior works, it employs GCNs with trequency-specife adjacency matrices, where each frequency band is associated with tailored con- nectivity to capture its distinct motion patterns, thereby enabling more effective modeling of motion dynamics across different bands.
4

---

Published as a conference paper at ICLR 2026
3.4 JOINT-ADAPTIVE NOISE GENERATOR.
Unlike previous diffusion-based methods that adopt a fixed schedule, our approach designs a learn- able noise schedule. We introduce a multivariate noise schedule that assigns cint-adaative noise rates, enabling the diffusion process to capture spatial variability in the human skeleton:
(ive 1/--1) = (yri 0 (1 a+)
(4)
where > diag(sī s3. .....,, $3), 5; denotes the noise scaling factor for the j-th joint. Specifically, the scaling factor is determined by two aspects. First, it depends on the joint index, since different joints exhibit distinct motion characteristics. Second, we further refine the scaling factor by condi- tioning on the historical motion trajectories of each joint. Formally, the joint- and instance speciffc noise scaling can be expressed as:
8 fo(i.x)1.1)
where ji is the joint index, xj (1:H)) denotes the observed motion history of joint j. and fo is a learnable function. Through this design, are able to inject noise with varying intensities across different joints, better reflecting their heterogeneous motion properties. The reverse diffusion process that denoises the corrupted data samples y/: from / = T down to 1:
Po (o yo) N (Mt-1: (2 x, t), (1 o1)
where = (sf s3...... 23)) 3j denotes the noise scaling factor for the j-th joint. Through the above design, the diffusion process is able to inject joint- and instance-adaptive noise, leading to more realistic and coherent human motion generation.
3.5 STRUCTURE-ALIGNET REGULARIZER
Earlier diffusion-based approaches (Chen et al., 2023; Sun & Chowdhary, 2024) treated human motion prediction straightforward application of diffusion, overlooking the intrinsic structural properties of human pose. For example, such methods fail to enforce structural constraints, such maintaining consistent bone lengths throughout motion generation. We proposed Structure- Aligned Regularizer, which effectively aligns the predicted human motion with historical motion patterns based on the human skeletal structure during the diffusion process. Specifically, we cal- culated the average bone length of connected joints from past human motions. Since the historical motion is noise-free, allows us to easily extract structural information. We regard the motion to be predicted as clean human motion plus Gaussian noise:
a+ yo + /I ā.
where c ~ (0, the mean across the batch. Since the noise e follows Gaussian distribution with zero mean, we can compute the bone lengths of the motion sequence y from this mean:
I) denotes Gaussian noise and yo is the clean motion. For batch of yı, we take
1 B
(b) Ut
U1
v
yo:
where is the batch size. Specifically, we denote the set of skeletal connections as E, where each (i,j) E E represents connected joint pair. The corresponding bone length is defined as:
h Ily' 1'll.. (i,j) E
where y², y 3R3 denote the 3D coordinates of joints and j.
As previously mentioned, our model predicts the future motion values directly at each step. When t is relatively large, the direct prediction yo tends to be inaccurate. To address this, we impose constraints the bone lengths of the target human motion and perform alignment with the structure of the historical human motion. Moreover, at each timestep, after the initial encoder, apply the same operation Yo ensure that the human skeleton structure remains consistent throughout the entire sequence. Specifically, for each bone connection (i,j) E E, we compute the average bone length the observed history frames x (1:H) ((6.)) and those of (1:F) and (1:P)
5

---

Published as a conference paper at ICLR 2026
Fried) and ) respectively. The alignment loss then defined as the mean discrepancy between the two sets of averaged bone lengths:
(1.) pred
((..)
((6-7) ref
Lalign
(10)
obs
abs
1
(iJ))EE
(A.))EE
which encourages the predicted skeleton to preserve the structural scale of the observed motion, thereby maintaining consistent bone proportions across time.
3.6 OVERALL LEARNING OBJECTIVES
Our loss consists of two components: a reconstruction loss applied to the predicted poses and an alignment loss enforcing consistency between the predicted human motion and observed human mo- tion. Unlike most diffusion- directly outputs pose prediction Do for every diffusion timestep t. This design allows pose the reconstruction and alignment loss at every timestep, ensuring that the denoising trajectory remains consistently aligned with anatomically plausible human motion rather than relying solely on the final-step supervision. For the reconstruction loss, we follow prior work (Sun & Chowdhary, 2024) and assign different weights individual joints, which are weighted differently to reflect their relative importance in the motion context. The reconstruction loss for each timestep is defined by:
motion models that predict noise at each timestep, our denoiser
im-
free Σ
7 11 x)) 221, 11:00 n66) X2 111.):
(11)
where the superscript j denotes the joint index, X is the weight assigned each joint, and y is hyperparameter balancing the reconstruction of motion history and the prediction of future.
Liotal = a Lree + 3 Calign
(12)
where a and 3 control the relative weight of the reconstruction and alignment losses.
4 EXPERIMENTS
We first introduce the experimental setup in $4.1. Then we assess the performance of our method across various settings, including intra-dataset forecasting on Human3. Human3.6M( 6M4.2),), and more chal- lenging cross-dataset generalization on AMASS(44.3). Lastly, we provide ablative analyses in $4.4.
4.1 EXPERIMENTAL SETUP
Datasets. We conduct experiments on two widely used datasets, intra-dataset forecasting on Hu- man3.6M (Ionescu et al., 2013) and cross-dataset generalization on AMASS (Mahmood et al., 2019).
Human3.6M is a seminal indoor dataset for 3D human motion analysis, widely utilized in stochas- tic Human Motion Prediction. It comprises 3.6 million frames, captured 50Hz, documenting 11 subjects performing 15 common daily activities. Consistent with prior work (Barquero et al., 2023), we delineate subjects $1, S5, S6, S7, S8 for training, and subjects $9, S11 for evaluation. AMASS is a large-scale, highly diverse motion dataset for assessing cross-dataset generalization. It consolidates 24 distinct motion capture datasets, all standardized to the SMPL parameterization, accumulating over million frames. Following prior research (Barquero et al., 2023), the dataset is partitioned into 11 training, 4 validation, and testing constituent datasets.
Implementation Details. Our model, is trained end-to-end, following protocols detailed below:
Diffusion Settings. KinemaDiff employs 10-step diffusion process with standard DDPM sam- pling, which is inherently augmented by our proposed ainematics-aware designs. Specifically, both the Prructure-Alineed Regularizer and the Joint-Adaptive Noise Generator integrated within the diffusion steps to ensure physical consistency and capture joint heterogeneity.

---

Published as a conference paper at ICLR 2026
Table I: Quantitative results on Human3. 6.M. The best results are highlighted in bold. The symbol
indicates not reported in the baseline work. For all metrics except for APD, lower is better.
Accuracy
Multimodality
Diversity
Realism
Method
Reference
ADE FDB MMADE MMEDE APD f CMD y
GAN-Based
HP-GAN (Barsoum et al., 2018)
[CMPRW2018)
0.858
0.867
0.847
0.858 0.545
7.214 6.509
DeLiGAN (Gurumurthy et al., 2017)
[CVP92017)
0.483 0.534
0.520
VAE-Based
TPK (Walker et al., 2017)
[CCC22017]
0.461
0,560
0.522
0,569
6.723
6.326
0.538
Motron (Salzmann et al., 2022)
[CVPR2022]
0.375
0,488
0.509
0.539
7.168
40.796
13.743
DSF (Yuan & Kitani, 2019)
[CCU83020]
0.493
0.592
0.550
0.599
9.330
Low (Yuan & Kitani, 2020)
[PCCV2020]
0.425
0.518
0.495
0.531
11.741
4.927
1,255
GSPS (Mao al., 2021)
[ICV2222)
0.389
0.496
0.476
0.525
14.757
10.758
2.103
DivSamp (Dang et al., 2022)
JACMMM2022
0.370
0.485
0.475
0.516
15.310
11.692
2.083
RMM-Based
MotionDiff (Wei et al., 2023)
[AAA1223
0.411
0.509
0.508
0,536
15.353
HumanMAC (Chen et al., 2023)
[[CCV202]]
0.369
0.480
0.509
0,545
6.301
BeLFusion (Barquero et al., 2023)
[[CCV2023]
0.372
0.474
0.473
0.507
7,602
5.988
0.209
CoMusion (Sun & Chowdhary, 2024)m00
0.350
0.458
0.494
0.506
7.632
3.202
0.102
SkeletonDiff (Curreli al., 2025)
[CVP92022]
0.344
0.450
0.487
0.512
7.249
4.178
0.123
Ours
0.331
0.449
0.500
0.320
6.912
4.60
70.083
Training Protocols. For both the Human3.6M and AMASS datasets, the model undergoes train- ing for 500 epochs. We utilize the Adam optimizer with a batch size of 128. The initial learning rate is set to le-4, which is subsequently decayed after the 200th epoch. These training parameters are consistent across both datasets.
Dataset-Specific Protocols.
Human3.6M. Consistent with prior work (Barquero al., 2023), we predict 100 future frames from 25 observed frames, using a 16-joint skeleton.
- AAMASS. Following the protocol established by (Barquero et al., 2023), the task involves fore- casting 120 future frames (2s) based 30 observed frames (0.5s).
Baselines. We compare our method against several representative diffusion based methods.
Fusiin.o.. Operating within a VAE-encoded latent space, BeLFusion employs a diffusion model generate diverse and behavior-driven future motion predictions. Nevertheless, its physics- based consistency check is a post-pin post-processing step applied externally, rather than a constraint that guides the iterative denoising process internally.
CoMusion. Integrating Graph Convolutional Networks (GCNs) within the Discrete Cosine Trans- form (DCT) space, CoMusion's 's denoiser effectively captures complex spatio-temporal dependen- cies. Though effective in modeling these dependencies, it focuses on advancing the architecture, rather than proposing adaptations to the core diffusion mechanism itself.
SkeletonDiffusion Introducing a non-isotropic diffusion process, SkeletonDiffussin defines an anisotropic noise covariance matrix derived from the skeleton's static kinematic tree. Though effective in acknowledging joint heterogeneity, its noise characteristics remain fixed and are not adaptive to the unique dynamics of given motion instance.
Evaluation Metrics. Following established practices, we evaluate our method across three key aspects: accuracy, diversity, and realism, using a comprehensive suite of standard metrics.
Accuracy. We report Average Displacement Error (ADE) and Final Displacement Error (FDE), the mean & distance to the GT over the sequence and at the final frame, respectively. Diversity and Multimodality. We take Average Pairwise Distance (APD) to measure the variance among generated samples. we also report Multimodal ADE/FDE (MMADE/MMFDE), which assess multimodality by measuring the error to the best-matching ground-trute variant.
Realism and Plausibility. We employ the Fréchet Inception Distance (FID) to assess the distri- butional similarity between generated and real motions. Additionally, following (Barquero al.,

---

Published as a conference paper at ICLR 2026
Table 2: Quantitative results for AMASS dataset. The best results are highlighted in bold. As
AMASS does not contain class labels, the FID metric is not used for evaluation.
Accuracy
Multimodality
Diversity Realism
Method
Reference
ADE 1 FDE MMADE MMFDE 1 APD CMD
VAF-Based
TPK (Walker et al., 2017)
[ICCV2017]
0.656
0.675
0.658
0.674
9.283
17.127
DLow (Yuan & Kitani, 2020)
(eCCv202))
0.590
0.612
0.618
0.617
13.170
15.185
GSPS (Mao et al., 2021)
[CCV2221]
0.563
0.613
0.609
0.633
12.465
18,404
DivSampp (Dang al., 2022)
[ACMMM2022]
0.564
0.647
0.623
0.667
24.724
50.239
DM-Based
HumanMAC (Barquero et al., 2023)
[ICCV2023]
0,511
0.554
0,593
0,591
9.321
BeLFusion (Barquero al., 2023)
[ICCV2023]
0.513
0.560
0.569
0.585
9.376
16.995
CoMusion (Sun & Chowdhary, 2024))
BCCV2024)
0.494
0.547
0.469
0.466
10.848
9.636
SiffetonDiff (Curreli et al., 2025)
[CVPRane))
0.480
0.545
0.561
0.580
9.456
11.417
Ours
0.478
0.540
0.456
0.457
9.683
9.448
Table Ablation of the main components in our Table 4: Experiment results on Hu-
method on Human3.6M,
man3. 666 with different Scheduler.
Encoder J-Noise Align
APD
ADE
FDE
FID
Scheduler APD ADE 4. FDE 4
FID
19.601
0.852
0.775
2.393
9.600
0.653
0.574
0.932
Sqrt
6,837
0.342
0.457
0.108
v
6.214
0.354
0.478
0.177
<
7.243
0.339
0.454
0.088
Cosine 7.213
0.365
0.478
0.178
V 7.014
0.336
0.453
0.089
/ 7
6.912
0.331
0.449
0.083
Variance
6.912
0.331
0.449
0.083
2023), we use the Cumulative Motion Distribution (CMD) area for global plausibility to evaluate how realistically the model's generated diversity reflects that of the ground truth.
4.2 RESULTS OF INTRA- DATASET FORECASTING ON HUMAN3.6N.
The comparative performance on the Human3.6N, dataset is systematically reported in Tab. 1. The results unequivocally demonstrate that our model establishes a new state-of-the-arr result in forecast- ing accuracy and realism. Specifically, KinemaDiff achieves an ADE of 0.331 and an FDE of 0.449, surpassing all prior methods. We attribute this superior accuracy to the peucture-Aligned Regu- larizer. By rigorously maintaining anatomical consistency at each step of the denoising process, this module prevents the accumulation of kinematic errors that can degrade long-term predictions, ensuring physically grounded and accurate motion trajectory.
Furthermore, the effectiveness of our approach generating high-fidelity motion is underscored by the FID score of 0.083, substantial 19% relative improvement over the previous leading model, CoMusion. A lower FID indicates that the distribution of generated motions is significantly closer to that of real human movements, not just in individual poses but in the naturalness of the entire sequence. This gain in realism is a direct result of the synergy between our two core modules: the Structure-Aligned Regularizer eliminates anatomically impossible poses, while the Joint-Adaptive Noise Generator sculpts more natural, heterogeneous joint movements, avoiding the robotic unifor- mity that can arise from conventional noise schedules. While other methods may achieve higher raw diversity scores (APD) or broader multimodal coverage (MMADE/FDE), KinemaDiff excels at ensuring that every generated sample possesses high physical fidelity, prioritizing the quality and plausibility of predictions as evidenced by its leading accuracy and realism metrics.
4.3 RESULTS OF CROSS-DATASER GENERALIZATION ON AMASS
To evaluate robustness and generalization, we present a comparative analysis on the diverse AMASS dataset in Tab. 2. In this challenging cross-dataset scenario. KinemaDiff demonstrates exceptional performance, achieving advanced results across the majority of metrics, including ADE (0.478), FDE (0.540), MMADE (0.456), MMFDE (0.457), and CMD (9.448). These results highlight
8

---

Published as a conference paper at ICLR 2026
0.200
ADE
FDE
0.38
0.52
0.150
0.36
0.48
0.34
0.100
Figure 4: Ablation results on the number of diffusion steps.
0.5w
1.0s
2.s
1.5%
2,0g
1.0s
1.5
205
CoMusion
Ours
Figure 5: Visualization results. The red-black skeletons and green-purple skeletons denote the ob- served and predicted motions respectively.
the model's ability to learn fundamental principles of motion rather than dataset-specifie artifacts. The superior generalization is primarily driven by the Structure-Aligned Regularizer, which learns intrinsic and invariant anatomical properties like bone lengths, making the model robust to the wide variety of novel motions present in AMASS. Concurrently, the results in multimodal metrics (MMADE/MMFDE) provide clear evidence for the efficacy of the Joint-Adaptive Noise Generator. On diverse dataset like AMASS, where single observation the ability to generate cestance-specffic heterogeneous noise allows the model to explore this rich possibility space
lead to many valid future actions,
effectively than methods with static uniform noise. It does generate random variations but rather meaningful and plausible alternatives tailored to the input context. While some methods achieve a higher raw diversity score (APD) the cost of accuracy, KinemaDiff achieves a competitive APD (9.683) while simultaneously delivering the best accuracy and multimodal coverage, demonstrating superior balance between diversity and fidelity.
merely
4.4 ABLATION STUDY
For in-depth analysis, we conduct ablative studies using Ettaa-Dtaset Forecasting Human3.6M
Effeet of our main components. To assess the contribution of our core components, we conducted an ablation study by removing the Stracture-Aligned Regularizer, joint-adaptive noise, and the ini- tial encoder. From Tab, 3, can be seen that using only the baseline or only the initial encoder leads low accuracy (ADE, FDE) and FID scores, despite high APD values. This suggests the model produces motions that are diverse yet largely implausible and unstructured. Incorporating the Structure-Aligned Regularizer substantially improves accuracy metrics (ADE, FDE) and sistency metrics (FID) by leveraging structural cues aligned with historical motion. In addition, the joint-adaptive noise dynamically allocates noise different joints, enabling more accurate modeling of human motion and further improving both accuracy (ADE, FDE) and consistency (FID) metrics.
Diffusion setting. We mainly investigate two aspects of the diffusion setting: the choice of sched- uler and the number of diffusion steps. We evaluate multiple scheduler choices on the Human3.06 6M dataset in Tab. 4, and the results indicate that Variance Scheduler (Sun Chowdhary, 2024) pro- vides the best trade-off between stability and performance. In addition, we investigate the effect of the denoising step on model performance. We evaluate multiple metrics on Human3.66 under different timesteps. As shown in Fig. 4, the model achieves the best performance when the number of timesteps is 10. Morcover, choosing 10 timesteps ensures fast inference and accuracy.
Visualization results. In Fig. 5, we present a qualitative comparison by visualizing predicted motion sequences on Human3. 6.M. We use CoMusion as the baseline and select 15 predicted results for each frame. The visual analysis shows that our method generates more consistent and realistic human motions. Compared with the ground truth, our predictions are especially accurate for samples with smooth movements. In addition, unrealistic artifacts, such sudden exaggerated leg lifts while

---

Published as a conference paper at ICLR 2026
Table 5: Comparison of physical realism metrics on Human3.6M
Method
Limb Stretch
Limb Jitter
ADE
FDE
FID
SkeletonDiffusion
3.90
0.16 0.45
0.344
0.450
0.123
KinemaDiff (Ours)
2.42
0.331
0.449
0.083
KinemaDiff + jitter loss
2.42
0.28
0.331
0.447
0.084
walking, occur less frequently. Moreover, the diversity of generated motions better reflects realistic dynamics, with predictions concentrated near historical patterns and gradually diffusing over time.
Analysis of Physical Realism Metrics. To quantitatively assess the physical plausibility of the gen- erated motions, we evaluate two additional metrics on Human3.6M Limb Stretch and Limb Jitter, as seen in Tab. 5. Our method achieves limb stretch of 2.42. which lower than SkeletonDiffu- sion's ' 3.90. This improvement is primarily attributed to our Structure- Structure-Aligned Aligned Regularizer, which constrains the skeleton at every diffusion timestep, effectively reducing unrealistic deformations. Besides, our model obtains limb jitter score of 0.45. To further explore the flexibility of our frame- work in enforcing temporal consistency, we adopted the same jitter loss as used in SkeletonDiffusion as an additional constraint. Specifically, this loss quantifies the magnitude of bone length changes between adjacent frames. We calculate the bone length Lę from 3D keypoints and define the jit- ter as the absolute difference | T./1- The loss minimizes the squared difference between the predicted and ground-truth jitter values, constraining the generated bone dynamics to be temporally consistent with real motion. After incorporating this loss, the jitter metric significantly improves from 0,45 to 0.28. This demonstrates that our framework can readily incorporate such constraints to prevent excessive high figh-freueecc shaking. Crucially, adding this loss does not compromise other metries (ADE / FDE / FID remain stable), confirming that our framework can effectively balance structural validity and temporal smoothness.
Effect of different loss functions in Structure-aligned Regularizer. To further investigate the im- pact of different structural regularization strategies, we evaluate two stricter frame-wise bone-length constraints based on L1 and L2 penalties, as seen in Tab. 6. The L1 formulation leads to sub- stantially degraded performance (ADE 0.457), indicating that its constant gradient may introduce instability during denoising. In contrast, the L2-based constraint enforces the geometry more rigor- ously and achieves the lowest FID, but slightly reduces prediction accuracy due to over-constraniin the motion manifold at each timestep.
Table 6: Effect of different loss functions in Structure-Aligne Regularizer.
Loss Variant
ADE 4
FDE :
FID 1
Frame-wise Lı
0.457
0.574
0.512
Frame-wise L2
0.333
0.452
0.075
Average (Ours)
0.331
0.449
0.083
5 CONCLUSION
In this work, we introduce new diffusion model tailored for stochastic human motion prediction. Our algorithm integrates a Joint-Adaptive Noise Generator and Structure- Alligne Regularizer di- rectly within the diffusion process. The former enhances motion diversity with instance-aware noise while the latter preserves anatomical consistency by embedding structural priors into each diffu- sion step. Results across multiple benchmarks demonstrate its effectiveness, owing to the unique integration of structural and dynamic priors within diffusion process.
10

---

Published as a conference paper at ICLR 2026
REFERENCES
German Barquero, Sergio Escalera, and Cristina Palmero. Belfusion: Latent diffusion for behavior- driven human motion prediction. In Proceedings of the IERE/CVF international conference computer vision, pp. 2317-2327, 2023.
Emad Barsoum, John Kender, and Zicheng Liu. Hp-gan: Probabilistic human motion prediction via gan. In Proceedings of the IEEE conference computer vision and pattern recognition workshops, pp. 1427, 2018.
Ling-Hao Chen, Jiawei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, and Tongliang Liu. Human- mac: Masked motion completion for human motion prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 9544-9555, 2023.
Cecilia Curreli, Dominik Muhle, Abhishek Saroha, Zhenzhang Ye, Riccardo Marin, and Daniel Cre- mers. Nonisotropic gaussian diffusion for realistic 3d human motion prediction. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 1871-1882, 2025.
Lingwei Dang, Yongwei Nie, Chengjiang Long, Qing Zhang, and Guiqing Li. Msr-gen: Multi- scale residual graph convolution networks for human motion prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 1467-11477, 2021.
Lingwei Dang, Yongwei Nie, Chengjiang Long, Qing Zhang, and Guiqing Li. Diverse human motion prediction via gumbel-softmax sampling from an auxiliary space. In Proceedings of the 30th ACM international conference on multimedia, pp. 5162-5171, 2022.
Jia Gong, Lin Geng Foo, Zhipeng Fan, Qiuhong Ke, Hossein Rahmani, and Jun Liu. Diffpoos: To- ward more reliable 3d pose estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15041-13057 2023.
I Liang-Yan Yan Gui, Kevin Zhang, Yu-Xiong Wang. Xiaodan Liang. José MF Moura, and Manuela Veloso. Teaching robots to predict human motion. 2018 IEEF/RSI International Conference on Intelligent Robots and Systems (IROS), pp. 562-567. IEEE, 2018.
Swaminathan Gurumurthy, Ravi Kiran Sarvadevabhatla, and R Venkatesh Babu. Deligan: Genera- tive adversarial networks for diverse and limited data. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 166-174, 2017.
Jonathan Ho, Ajay Jain, and Picter Abbeel. Denoising diffusson probabilistic models. Advances in neural information processing systems, 33:6840-6851, 2020.
Xingchang Huang, Corentin Salaun, Cristina Vasconcelos. Christian Theobalt, Cengiz Oztireli, and Gurprit Singh. Blue noise for diffusion models. In ACM SIGGRAPH 2024 conference papers, pp. 1-11, 2024.
Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu. Human3. 6m: Large scale datasets and predictive methods for 3d human sensing natural environments. IEEE transactions pattern analysis and machine intelligence, (6(7):1325-1333 2013.
Ashesh Jain, Amir R Zamir, Silvio Savarese, and Ashutosh Saxena. Structural-rnn Deep learning on spatio-temporaa graphs. Proceedings of the ieee conference on computer vision and pattern recognition, pp. 1308-5317, 2016.
Maosen Li, Siheng Chen, Zijing Zhang, Lingxi Xie, Qi Tian, and Ya Zhang. Skeleton-parter graph scattering networks for 3d human motion prediction. In European conference on computer vision, pp. 18-18888 36. Springer, 2022.
Han Liang, Wenqian Zhang, Wenxuan Li, Jingyi Yu, and Lan Xu. Intergen: Diffusion-based multi- human motion generation under complex interactions. International Journal of Computer Vision, 13209):3433-348, 2024.
Tiezheng Ma, Yongwei Nie, Chengjiang Long, Qing Zhang, and Guiqing Li. Progressively gen- erating better initial guesses towards next stages for high-quality human motion prediction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 6437- 6446, 2022.
11

---

Published as a conference paper at ICLR 2026
Naureen Mahmood, Nima Ghorbani, Nikolaus F Troje, Gerard Pons-Moll and Michael J Black. Amass: Archive of motion capture as surface shapes. In Proceedings of the IEEE/CVF international conference computer vision, pp. 5442-5451, 2019.
Wei Mao, Miaomiao Liu, and Mathieu Salzmann. Generating smooth pose sequences for diverse hu- man motion prediction. In Proceedings of the IEEE/CVE International Conference on Computer Vision, pp. 13318, 2021.
Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In International conference on machine learning, pp. 8162-8171. PMLR, 2021.
Brian Paden, Michal Cáp, Sze Zheng Yong, Dmitry Yershov, and Emilio Frazzoli. A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Transactions intelligent vehicles, 111):33-55, 2016.
Subham Sahoo, Aaron Gokaslan, Christopher M De Sa, and Volodymyr Kuleshov. Diffusion models with learned adaptive noise. Advances in Neural Information Processing Systems, 37:105730- 105779, 2024.
Tim Salzmann, Marco Pavone, and Markus Ryll. Motron: Multimodal probabilistic human mo- tion forecasting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6457-6466 2022.
Wenkang Shan, Zhenhua Liu, Xinfeng Zhang, Zhao Wang, Kai Han, Shanshe Wang, Siwei Ma, and Wen Gao. Diffusion-based 3d human pose estimation with multi-hypothesis aggregation. In Proceedings of the IEEE/CVF International Conference Computer Vision, pp. 14761-14771, 2023.
Jiaming Song. Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. preprint arXiv:2010.02500. 2020.
arXiv
Jiarui Sun and Girish Chowdhary. Comusion: Towards consistent stochastic human motion predic- tion via motion diffusion. In European Conference Computer Vision, pp. 18-36. Springer, 2024.
Jacob Walker, Kenneth Marino, Abhinav Gupta, and Martial Hebert. The pose knows: Video forecasting by generating pose futures. In Proceedings of the IEEE international conference on computer vision, pp. 3332-3341, 2017.
Dong Wei, Huaijiang Sun, Bin Li, Jianfeng Lu, Weiqing Li, Xiaoning Sun, and Shengxiang Hu. Human joint kinematics diffusioon-efinement for stochastic motion prediction. In Proceedings of the AAAI Conference Artificial Intelligence, volume 37, pp. 6110-6118, 2023.
Chenxin Xu, Robby T Tan, Yuhong Tan, Siheng Chen, Xinchao Wang, and Yanfeng Wang. Auxil- iary tasks benefit 3d deeleton-based human motion prediction. In Proceedings of the IEEE/CVF international conference computer vision, pp. 9509-9520, 2023.
Guowei Xu, Jiale Tao, Wen Li, and Lixin Duan. Learning semantic latent directions for accurate and controllable human motion prediction. In European Conference on Computer Vision, pp. 56-73. Springer, 2024.
Sirui Xu, Yu-Xi fu-Xiong Wang, and Liang-Yan Gui. Diverse human motion prediction guided by multi- level spatial- temporll anchors. In European Conference on Computer Vision, pp. 251-269. Springer, 2022.
Ye Yuan and Kris Kitani. Diverse trajectory forecasting with determinantal point processes. arXiv preprint arXiv: 10w.1990..0967, 2019.
Ye Yuan and Kris Kitani. Dlow: Diversifying latent flows for diverse human motion prediction. In European Conference on Computer Vision, pp. 346-364. Springer, 2020.
12

---

Published as a conference paper at ICLR 2026
A APPENDIX
The supplementary material herein extends the discussion and analysis presented in the primary manuscript. It is structured as follows:
Ethics statement. ($ A.I): This section describes ethics statement of the manuscript.
Reproducibility Statement. ($ A.2) This section describes reproducibility statement of the manuscript.
Use of Large Language Models ($ A.3): This section describes the use of large language models across the manuscript.
Additional Visualization ($ A.4): : This section introduces additional visualization results.
Additional Hyperparameter Setting ($ A.5) This section introduces additional hyperparameter
setting.
Additional Metric Descriptions ($ A.6): This section introduces the details of the metrie descrip- tions used in the manuscript.
Comparison with anisotropic noise ($ A.7): This section introduces a comparison with anisotropic noise and more detailed effect of the Joint-Adaptive Noise Generator.
Comparison with Standard Bone-Length Constraints ($ A.8): This section introduces the com- parison with standard bone-length constraints.
Discussion of learned noise scale ($ A.9): This section discusses the learned noise scale.
Discussion on realism/accurrayy and diversity of our model ($ A.10): This section analyzes how enforeing anatomical consistency leads high-quality, physically plausible motion diversity, even if numerical diversity (APD) is slightly reduced.
Effectiveness of Tinres- Structural Constraint ($ A.11): This section demonstrates why applying structural constraints at early timesteps is effective.
A.1 ETHICS STATEMENT.
This work focuses on human motion prediction using publicly available benchmark datasets (Hu- man3.6M and AMASS), which were collected and released under established research protocols. No personally identifiable or sensitive information involved, and our methodology does not in- volve human subjects, sensitive attributes, or private data, posing no privacy security concerns. experiments follow ethical research practices, including proper citations, fair comparisons with prior works, and reproducibility efforts. All authors have read and will adhere to the ICLR Code of Ethics.
A.2 REPRODUCIBILITY STATEMENT.
We have made significant efforts to ensure the reproducibility of our work. A detailed description of our model architecture is provided in Section 3, while the evaluation protocols and training setup are presented in Section 4 of the main paper. Additional implementation details are included in the Ap- pendix. All datasets used our experiments are publicly available and widely adopted benchmarks, and our preprocessing steps strictly follow prior works to ensure consistency and comparability.
A.3 THE USE OF LARGE LANGUAGE MODELS (LLMs)
In preparing this manuscript, we used a large language model (LLM) only for grammar correction and improving the clarity of phrasing. The LLM was not involved in generating ideas, methods, experiments, analyses, or results. All scientific contributions, including the problem formulation, model design, and evaluation, entirely the work of the authors.
13

---

Published as a conference paper at ICLR 2026
0.200
ADE
FDE
0.38
0.52
0.150
0.36
0.48
0.34
0.100
APD
MMADE
CMD
10
0.52
7-
0.50
10
30
10
20
50
20
30
Figure 6: Additional ablation results on the number of diffusion steps.
A.4 ADDITIONAL. VISUALIZATION.
To further validate the physical realism and accuracy of our generated motions, we provide ad- ditional qualitative comparisons with baselines including CoMusion (Sun & Chowdhary, 2024), SkeletonDiffusion (Curreli al., 2025), and BeLFusion (Barquero al., 2023). As illustrated the accompanying figures (Fig. 7), KinemaDiff consistently produces results closer to the ground truth with fewer unrealistic poses compared CoMusion. Moreover, significantly reduces artifacts like bone stretching common in SkeletonDiffusion and BeLFouioo. These visual improvements sub- stantiate the effectiveness of our approach, demonstrating that our proposed structural constraints and adint-adaptive noise generator effectively enforce anatomical consistency and improve motion coherence without compromising diversity.
A.5 ADDITIONAL HYPERPARAMETER SETTING.
Additional diffusion setting. We introduce three additional metrics APD, MMADE, and CMD-to evaluate model performance across different timesteps. As shown in Fig. 6, when the number of timesteps set to 10, the model achieves strong performance in terms of diversity, aceu- racy, and consistency. Therefore, we select this setting for our experiments.
Hyperparameter for loss function. In Section 3.6, set 7 0.1 to balance the reconstruction of motion history and the prediction of future frames. The coefficients and 3 are set to and 2, respectively, control the relative contributions of the reconstruction loss and the alignment loss. These values were selected based on experiments with several parameter configurations to identify the most effective setting.
Model setting, In the initial encoder, we stack two Transformer layers with a feature dimension of 512. In the Structure- Aligged Regularizer, we employ nine Frequency-aware GCN layers with feature dimension of 125. The graph structure consists of N nodes, where N corresponds to the number joints the skeleton.
A.6 ADDITIONAL. METRIC DESCRIPTIONS.
APD (Average Pairwise Distance) measures the diversity of generated samples by com- puting the average distance between all pairs of generated motions.
ADE (Average Displacement Error) computes the the predicted and ground-truth motions, reflecting overall accuracy.
per-timestep distance between
FDE (Final Displacement Error) measures the distance between the predicted and ground-truth motions at the final timestep, highlighting long-term prediction accuracy.
MMADE and MMFDE extend ADE/FDE by comparing with clustered groundtruth vari- ants, capturing model's ability generate multiple plausible outcomes.
14

---

Published as a conference paper at ICLR 2026
0.5s
1.0s
1.58
2,0s
0.55
1.0g
1,55
2.06
0.5c
1.06
1.56
2,06
4
Fusion
A
SkeletonDiifluion
CoMusion
Ours
0.5s
1.0%
1,5a
2.04
0.5h
1.04
1,55
2.06
0.5s
BeLFousion
SkeletonDifluion
CoMusion
Ours
Figure 7: Additional visualization results. The red-black skeletons and green-purple skeletons de- note the observed and predicted motions respectively.
CMD (Conditional Motion Distance) quantifies global plausibility by comparing the ar- eas under cumulative distributions of true and generated motion.
FID (Fréchet Inception Distance motion) computes the distance between the feature distributions of generated and ground-truth motions, reflecting realism the distribution level.
As shown in Tab. 7, we present the formulas for the metrics used in the main text, with the definitions of relevant parameters as follows.
Notation. We denote the ground-truth motion sequence p {pp )_ and the k-th generated
motion sequence by p (k) {p,
(R) (2) d where T' is the prediction horizon and K is the number of
generated samples. Here, Pt R /x3 represents the 3D skeleton at timestep f, with J denoting the
number of joints. For CMD, we compute the average displacement of all joints at frame t as M,, and the average displacement over the whole test set as M. (μ,, Σ) are the mean and covariance of extracted motion features used for FID calculation.
A.7 COMPARISON WITH ANISOTROPIC NOISE.
Although our J-Noise shares similarities with SkeletonDifflssinn (Curreli et al., 2025) anisotropic noise, they differ fundamentally in design. SkeletonDiffusion treats the human skeleton as pre- defined graph with fixed, manually specified wosnt-wise covariance. In contrast, J-Noise learns dynamic scale s, = fe(j. x):H) directly from data, making the injected noise both joint-dependent and motion-dependent.
To evaluate the contribution this proposed module, we conduct a detailed ablation study compar- ing the full model with two reduced variants: (1) No J-Noise, which adopts uniform scalar noise schedule shared across all joints, and (2) No Temporal, which learns static per-joint noise scales but omits temporal motion cues.
15

---

Published as a conference paper at ICLR 2026
Table 7: Evaluation metrics used for motion prediction.
Metric
Formula
T K(K
1
APD
P
1)
T
icj
1
ADE
Dr: -P.
T 1=1
FDE
|PT 11112
T-1
CMD
Σ (T - t) || - M11 ==1
FID*
1rl1 11 Tr ( -2(=,,,,1
The results in Tab. 8 highlight the importance of both joint-aware and temporally adaptive noise modeling. The No J-Noise variant exhibits the highest diversity (APD) but suffers from signifi- cantly degraded accuracy (ADE/FDE) and realism (FID), as the identical noise assignment fails to distinguish between highly dynamic limb joints and more stable trunk joints. Introducing joist noise scaling (No Temporal) alleviates this issue and leads to measurable improvements, yet the model still underperforms compared to the full version.
In contrast, the Full J-Noise module by adapting noise magnitude based on both joint identity and motion history, achieves the best performance across accuracy and physical realism metrics, while maintaining competitive diversity. These findings confirm that adaptive, temporally modulated joint- wise noise regulation is essential for generating accurate, coherent, and physically plausible motion.
Table 8: Detailed ablation the Aint-Addptive Noise Generator. The full model achieves the best accuracy and realism while maintaining competitive diversity.
Method
ADE
FDE
FID
APD
No J-noise
0.339
0.454
0.088
7.243
No temporal
0.336
0.452
0,086
7.041
Full J-Noise (ours)
0.331
0.449
0.083
6.912
A.8 COMPARISON WITH STANDARD BONE-LENGTH CONSTRAINTS.
Although bone-length consistency is a commonly used constraint in human motion genera- tion (Liang et al., 2024), our approach differs in how it is integrated into the diffusion model. Instead of predicting noise, the denoiser designed to output the denoised human motion ýu at every diffu- sion timestep. After the initial encoding stage, all subsequent operations occur directly in the space of a noisy but valid 3D skeleton, which enables anatomical constraints to be applied at every stage of the denoising process.
Prior methods such as InterGen (Liang et al., 2024) enforce bone-length consistency only on the final reconstructed motion sequence, meaning the constraint influences the model once the end of generation. In contrast, our step-wise formulation exposes the model to structural information during the entire denoising trajectory, encouraging globally consistent pose generation.
To assess the impact of this design, we replace our stse-wise constraint with the traditional final-step constraint. As shown in Tab. 9, enforcing structure every timestep yields consistently better accu- racy and realism, demonstrating the clear benefit of integrating anatomical consistency throughout diffusion.
16

---

Published as a conference paper at ICLR 2026
Table 9: Comparison with Standard Bone-Length Constraints.
Constraint Timing
ADE
FDE 4
FID
Final step Each step (Ours)
0.336
0.455
0.089
0.331
0.449
0.083
A.9 DISSCUSSION OF LEARNED NOISE SCALES
To investigate what is learned by the Joint- Adaptive Noise Generator, we visualize the learned joint- wise noise scales and find that they correlate with each joint's motion characteristics.
We categorized joints into three groups: Trunk (indices 0, 3, 6, 7, 8, 9, 10, 13), Knees & Elbows (1, 11, 14), and Wrists & Feet (2, 5, 12, 15). We compared three settings:
Ours: Initialized with increasing scales (1.0, 1.2, 1.4) and a learnable offset (clamped to [- 0.3, 0.3), encouraging higher variance for dynamic extremities.
Uniform: Fixed scale of 1.0 for all joints.
Reverse-scale: Initialized with decreasing scales (1.0, 0.8, 0.6) and restricted learnable offset (clamped to [-0.2, 0.2)), foreing lower variance for extremities.
Analysis of Learned Scales: As shown in Tab. 10 and Fig. 8, our model learns to assign significantly
higher noise scales to dynamic joints (c.g., Joint 15/R-Wrist 1.41, Joint 5/L-Foot: 1.37) compared to stable trunk joints (e.g.. Joint O/Hip: 1.02). This aligns with the intuition that extremities have higher degrees of freedom and require more stochasticity. In contrast, the "Reverse-scale" setting forces the opposite pattern, suppressing noise in the limbs.
Performance Analysis: The impact of these noise distributions is clearly reflected in the quantitative results in Tab. 11. Ours achieves the best performance (ADE 0,331, FID 0.083), confirming that the learned kinematic-aware noise schedule effectively models human motion dynamics. Notably, severee-scale performs significantly worse (ADE 0.349, FID 0.115) than even the Uniform baseline. This degradation indicates that incorrectly assigning low variance dynamic joints (and high vari- ance to stable ones) actively harms the generation process, further validating the necessity of our joint-adaptive design.
Table 10: The Learned Noise Scale per Joint in different noise settings on Human3 6M.
Joint Index
0
1
4
5
6
7
8
9
10
11
12
13
14
15
Ours
1.02
1.15
1.28
1.04
1.13
1.37
1.01
1.11
1.03
1.04
1.06
1.28
1.36
1.11
1.21
1.41
Reverse
0,98
0.84
0.73
1.00
0.87
0.75
1.05
1.09
1.07
1.11
1.07
0.83
0.71
1.06
0.85
0.73
Uniform
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Table 11: Effect of different joint noise settings.
Method
ADE
FDE 4
FID
Uniform
0.339
0.454
0.088
Reverse scale
0.349
0.462
0.115
Ours
0.331
0.449
0.083
A.10 DISCUSSION ON REALISM/ACCURACT AND DIVERSITY OF OUR MODEL.
Tab. shows that KinemaDiff achieves state-of-the-ar accuracy and realism, while its APD score is slightly lower than several baselines. This phenomenon does not indicate a limitation of the noise generation module, but rather reflects a fundamental characteristic of enforcing anatomical consis- tency. Methods that report higher APD values often achieve diversity by producing motion samples that violate physical constraints, such as unrealistic bone stretching or implausible joint trajectories. In contrast, the incorporation of both Structure-Alignet Regularizer and the Joint-Adaptive Noise
17

---

Published as a conference paper at ICLR 2026
pint-specific noise scales
Deverse scaes litifom sraies
10
00
066
0.4
0.2
00
pllt
Jniey
pind
bein
Alne
Mitne
Alint
Figure 8: Visualization of the learned noise scales compared to Uniform and Reverse-scall baselines on Human3, 6.. Our method automatically learns to assign larger noise scales to dynamic limb joints (indices 2, 11, 12. 14, 15) to capture their higher degrees of freedom, while maintaining lower variance for the stable trunk. In contrast, the Reverse-scale setting enforces unnatural distribution by suppressing noise in the extremities.
Generator restricts the generative distribution to anatomically feasible regions of the motion mani- fold. This "pruning" effect naturally leads to a more compact-yer physically valid-set of possible futures. As result, KinemaDiff exhibits slightly reduced numerical diversity while achieving sub- stantially stronger motion realism, evidenced by its state-of-the-art FID performance. These findings highlight that our objective is not unrestricted diversity, but high-quality and physically plausible diversity, which more faithfully reflects the true distribution of human motion.
A.11 EFFECTIVENESS OF EARLY-TIMESTED STRUCTURAL CONSTRAINT.
We analyse the feasibility and benefits of applying structural constraints at early diffusion timesteps 1). While the predieted êP early stages (c.g., 0.95) exhibits high uncertainty and lacks high-frequency details compared to later stages (1 < 0.3), applying structural constraints throughout the process is supported by the following rationale:
Ty Prediction. Unlike models that predict noise c, ours directly predicts the clean motion zi every step. This means the network explicitly estimates full skeleton even high noise levels. While the motion trajectory (pose) might be uncertain or coarse at / = 0.95, the skeletal structure (bone lengths) is deterministic property that should remain invariant.
Feasibility of Direct
Visualization Analysis. We added the output results for timesteps 1, 3, 6, and 8 (Fig. 9). Our visualizations show that even early timesteps like 3, our model can produce a rough prediction. Specifically, for short-term predictions, the outputs at timesteps I or 3 are already close the ground truth. Moreover, these early timesteps, the top-performing hypotheses are fairly close to the ground truth, even for long sequence predictions. Therefore, within our direct prediction framework for zig, imposing constraints the early denoising stages meaningful.
Quantitative Analysis. As shown in Tab. 12, our model produces reasonably structured out- puts rather than random noise even early steps. This further confirms that applying structural constraints is valid and feasible throughout the process.
Applying constraints early acts as a strong geometric regularizer. By enforcing bone-length consistency, we decouple structure from dynamics. We essentially tell the model: "Even you are unsure about the exact future pose, the output must be valid human skeleton." This effectively prunes the search space, forcing the denoising trajectory to evolve strictly within the space of anatomically valid poses and preventing the model from wasting capacity physically impossible distortions (e.g., stretched limbs).
Reducing Search Space via Structural Constraints.
18

---

Published as a conference paper at ICLR 2026
0.5g
1.06
1,56
2.06
0,55
1.04
1,0s
1,56
2.0
1 Step
3rd Step
6th Step
8hh Step
10th Step
(Final Pred.)
Figure 9: Performance across diffusion timesteps. The model achieves reasonable performance even at early denoising stages, with progressive improvement as the process proceeds to Step 10.
Table 12: Performance metrics across diffusion timesteps. The model achieves reasonable per-
formance even early denoising stages (Steps 1-3), with progressive improvement the process proceeds to Step 10.
Step
ADE
FDE
FID J.
1st Step
0.556
0.789
5.977
3rd Step
0.443
0.632
3.728
6th Step
0.358
0.490
0.763
8th Step
0.336
0.455
0.139
10th Step (Ours)
0.331
0.449
0.083
Specialized Variance Schedule. Enforcing constraints at very early timesteps could interfere with learning when the signal-to-noise ratio is extremely low. To address this, we intentionally adopt variance scheduler that suppresses noise magnitude when approaches the highest-noise region. Specifically, as shown in Tab. 13, our noise variance (vT - (i) is significantly smaller than that of the Cosine and Sqrt schedules in early timesteps (e.g., 0.707 vs. 0.986 timestep 1). This design is crucial: by reducing the noise level at early stages, the model can more easily recover meaningful skeletal structure from the noisy input, making feasible apply structural constraints effectively. In contrast, attempting to enforce constraints when predicting from near-complete noise (as Cosine/Sqrt schedules) would significantly more challenging and could hinder learning- Our approach ensures stable convergence and effective learning throughout the denoising process, as demonstrated in Tab. 4.
Comparison with Late-Stage Constraint. We conducted experiments applying the structural con- straint only during the late denoising stages < 0.2 and t < 0.3) Tab. 14, as is commonly done in other frameworks. In our direct prediction framework, imposing the constraint only on the last few timesteps still performs worse than applying it at every timestep. As shown in the table, restricting the constraint to late timesteps results in higher bone stretching (3.7 and 3.4 vs. 2.4) and degraded performance across all metrics compared to the full-process approach. This indicates that, for the direct prediction framework, waiting until the end enforce structure is suboptimal.
19

---

Published as a conference paper at ICLR 2026
Table 13: Comparison Noise Schedules.
The values represent the noise standard deviation
(v1 (4) at each diffusion timestep.
TimeStep
3
5
7
10
Cosine
0.986
0.948
0.887
0.805
0.703
0.584
0.451
0.307
0.155
0.005
Sqrt
0.831
0.747
0.676
0.609
0.544
0.477
0.406
0.327
0.228
0.007
Ours (Variance)
0.707
0.650
0.588
0.523
0,454
0.383
0.309
0.233
0.156
0.079
Table 14: Comparison with Late-Stage Constraints.
Constraint Timing ADE
FDE
FID : Stretch
<<
0.336
0.458
0.113
3.7
0.3
0.335
0.452
0.106
3.4
Ours (All steps)
0.331
0.449
0.083
2.4
20


Reviews:
[{"review_id": "BMI1Vp0Rnj", "reviewer": "Reviewer_v3Dk", "summary": "This paper presents KinemaDiff, a diffusion-based framework for stochastic human motion prediction. The primary idea is to embed kinematic heterogeneity and skeletal consistency directly within the denoising process. Two components are introduced:\n\n**Joint-Adaptive Noise Generator** — assigns heterogeneous Gaussian noise scales to each joint and dynamically modulates them using temporal features derived from the observed motion history.\n\n**Structure-Aligned Constraint Enforcer** — enforces bone-length invariance at each denoising step based on topology extracted from past motion sequences.\n\nExperiments on Human3.6M and AMASS demonstrate superior performance over recent baselines in accuracy and realism, supported by ablation studies and qualitative visualizations.", "questions": "1. I’d be curious to see a clearer ablation of the J‑Noise design — for instance, what’s the gain if you just use per‑joint independent noise instead of uniform noise, and then how much extra does adding the temporal information actually help?\n\n2. Can you explain a bit more how your Structure‑Aligned Constraint Enforcer is different from the usual bone‑length consistency losses used in motion generation? It might also be worth running a simple ablation to show its specific impact.\n\n3. Would it be possible to add visual comparisons with a couple more baselines, like SkeletonDiffusion and BeLFusion, so the realism claim is backed by broader evidence?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "Strong state-of-the-art results on Human3.6M and AMASS in terms of accuracy and realism.\n\nEffective integration of heterogeneous noise and skeletal constraint within the generation process, improving physical plausibility.\n\nComprehensive experiments, including ablations, quantitative benchmarks, and qualitative visualization, with consistent performance gains.\n\nProcess-level enforcement of anatomical constraints mitigates unrealistic poses that prior methods often handle via post-processing.", "weaknesses": "1. The Joint-Adaptive Noise Generator is quite similar to SkeletonDiffusion[1]’s anisotropic noise, with the main addition being a temporal feature to adjust the scale per joint.\n\n2. The Structure-Aligned Constraint Enforcer uses the common bone-length consistency idea seen in prior motion generation work (e.g. [2]), mainly changing it to be applied during each diffusion step.\n\n3. The gains in quantitative metrics are modest, and the visual results are only compared with CoMusion, which limits the strength of the realism claim.\n\n[1]Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction, CVPR25\n[2]InterGen: Diffusion-based Multi-human Motion Generation under Complex Interactions, IJCV2024", "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": "plN7jy8mZ9", "reviewer": "Reviewer_grBA", "summary": "This paper introduces KinemaDiff, a novel diffusion-based framework for stochastic human motion prediction designed to improve physical plausibility and motion diversity. Conventional methods often suffer from anatomical inconsistencies and ignore heterogeneous joint dynamics. KinemaDiff addresses this by integrating two core modules directly into the diffusion process: (1) a Joint-Adaptive Noise Generator, which learns to inject instance-aware, heterogeneous noise for each joint based on its specific dynamics and motion history, and (2) a Structure-Aligned Constraint Enforcer, which preserves anatomical consistency by embedding skeletal topology (i.e., bone lengths) as an alignment loss during the denoising process. Experiments on the Human3.6M and AMASS datasets demonstrate that KinemaDiff achieves state-of-the-art performance, particularly in prediction accuracy (ADE/FDE) and realism (FID/CMD).", "questions": "Regarding the \"Constraint Enforcer\": Did you experiment with a stronger, frame-wise alignment loss? For example, penalizing the L1 or L2 deviation from the observed bone length at each predicted future frame $f \\in [1, F]$, rather than just penalizing the deviation of the sequence-level average? If so, how did this impact training stability and the final metrics?\n\nCould you please clarify the exact mechanism described in Section 3.5? Is the alignment loss computed and backpropagated at every denoising timestep $t$ during training? Or is it only applied once to the final prediction $\\hat{y}_0$, as suggested by Equation 12?\n\nFor the Joint-Adaptive Noise Generator, the function $f_{\\theta}$ maps the joint index $j$ and its full history $x_j^{(1:H)}$ to a scaling factor $s_j$. Given that the paper describes this as \"a few linear layers\", how is the variable-length temporal history $x_j^{(1:H)}$ aggregated into a fixed-size vector to be processed by these layers?\n\nThe results show a trade-off between realism/accuracy and diversity (APD). In your view, is this lower diversity a necessary consequence of enforcing strict anatomical constraints (i.e., the model correctly \"prunes\" unrealistic but diverse motions), or do you see potential in the Joint-Adaptive Noise module to further enhance diversity while maintaining the high level of realism?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper's primary strength lies in its fundamental adaptation of the diffusion process itself, rather than merely modifying the denoiser's network architecture. It embeds kinematic and anatomical priors directly into the noising and denoising steps.\n\nThe Joint-Adaptive Noise Generator is a significant innovation. Moving beyond uniform or static anisotropic noise, this module learns a noise schedule conditioned on both the joint index and its specific motion history. This is more physically grounded, as different joints (e.g., wrist vs. hip) and different motions (e.g., walking vs. jumping) inherently have different degrees of freedom and stochasticity.\n\nThe Structure-Aligned Constraint Enforcer directly tackles a major failure mode of generative models, physical implausibility (e.g., stretching bones). By defining an explicit alignment loss based on bone lengths from observed motion, the model is strongly guided to produce anatomically consistent skeletons.\n\nThe method achieves state-of-the-art results on two standard benchmarks (Human3.6M and AMASS). The improvements in accuracy metrics (ADE/FDE) and, most notably, realism metrics (FID/CMD) are substantial, validating the effectiveness of the proposed components.\n\nThe ablation in Table 3 clearly demonstrates the contribution of each module. The Structure-Aligned module (\"Align\") provides the most significant boost in accuracy (ADE/FDE) and realism (FID), while the Joint-Adaptive Noise (\"J-Noise\") further improves these metrics, confirming their synergistic value.", "weaknesses": "The name \"Structure-Aligned Constraint Enforcer\" is a misnomer. The method described is a soft constraint or regularizer implemented via a loss function. It \"encourages\" anatomical consistency during training but does not \"enforce\" it, meaning it does not mathematically guarantee that the final output will be physically plausible.\n\nThe alignment loss is defined as the discrepancy between the average bone length over the observed history and the average bone length over the entire predicted future sequence. This is a very weak constraint. It allows for physically impossible motions (e.g., a bone shrinking in one frame and stretching in another) as long as the average length over the sequence is correct. A true physical constraint would require bone lengths to be (nearly) constant at every predicted frame.\n\nSection 3.5 is confusing. It states, \"at each timestep, after the initial encoder, we apply the same operation on $y_0$ to ensure that the human skeleton structure remains consistent\". This implies the alignment loss is applied at every step $t$ of the denoising process. However, the overall loss function and the definition of the alignment loss based on the final prediction suggest it is only applied at the end of the training step. This ambiguity is critical for reproducibility.\n\nThe results (e.g., Table 1) show that while KinemaDiff excels in accuracy and realism, its diversity score (APD) is lower than several baselines. While this is a common trade-off, the paper could benefit from a deeper discussion on whether this is a fundamental consequence of enforcing stricter physical realism or a limitation of the current noise generation module.", "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": "5NPivd6Jb0", "reviewer": "Reviewer_vAUr", "summary": "The paper proposes a method to address stochastic human motion prediction, particularly by integrating anatomical consistency into the generative diffusion process. The paper proposes: (a) a Joint-Adaptive Noise Generator, which learns noise scheduler specifically tailored for different join, to take into account its characteristics in the human dynamic, (b) a Structure-Aligned Constraint Enforcer, which integrates the bone length information into the diffusion process by a training loss, enforcing that the avarage length of the bones in the prediction aligns with the prediction in the observed past. The paper compares with relevant state-of-the-art methods, showing promising results on AMASS and H3.6M.", "questions": "Currently, I slightly lean toward rejection: I find the proposed modification interesting, but it is also quite limited as a contribution, and some evidence is still missing to fully establish its validity. For the rebuttal, I'd like to see the following questions addressed:\n\n1) Is it possible to clarify the \"Structure-Aligned Constraint Enforcer\" contribution, in light of the weaknesses above?\n2) Would it be possible to see the metrics about limbs jittering and scratching for your comparison setting?\n3) Would it be possible to include the ablation where only the Enforcer is removed?\n4) A minor question lies in the nature of the choice of not relying on a latent diffusion, though the J-Noise module could also be applied there. Is there any reason or intuition behind this choice?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The idea of learning noise schedulers for different individual joints is interesting, and as far as I know, not yet explored\n- The presentation is clear, and the paper is easy to follow.", "weaknesses": "- The \"Structure-Aligned Constraint Enforcer\" seems to be a simple loss that penalizes the error in the bones' length between the observed past and the prediction. Hence, its contribution seems a bit overstated in the paper, as well as the name is a bit deceptive. Also, in the ablation, it is not reported the case where Encoder and J-Noise are activated, but the Enforcer is not. This would be important to actually assess the contribution of this component.\n\n- Although the paper introduces a loss specifically to take into account bone length, there are no metrics to measure this aspect.  This is surprising, as Skeleton Diffusion actually proposed to measure limb stretch and jittering across the prediction, since it has been observed that more diverse results correlate with unrealistic deformation of the skeleton. \n\n- The paper does not include an analysis of what is learned by the Joint-Adaptive Noise Generator. I would find it interesting, as it can provide insights and interpretability in the proposed framework.\n\nMINOR:\nThe work does not discuss/compare with [1,2]\n\n[1]: Xu, S., Wang, Y. X., & Gui, L. Y. (2022, October). Diverse human motion prediction guided by multi-level spatial-temporal anchors. In European Conference on Computer Vision (pp. 251-269). Cham: Springer Nature Switzerland.\n\n[2]: Xu, G., Tao, J., Li, W., & Duan, L. (2024, September). Learning semantic latent directions for accurate and controllable human motion prediction. In European Conference on Computer Vision (pp. 56-73). Cham: Springer Nature Switzerland.", "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)