WaveAR: Wavelet-Aware Continuous Autoregressive Diffusion for Accurate Human Motion Prediction
Authors: shengchuan gao
Shuo Wang
Yabiao Wang
Ran Yi
Venue: NeurIPS 2025 poster ()
Keywords: Human motion prediction
Primary area: deep_learning

Abstract: This work tackles a challenging problem: stochastic human motion prediction (SHMP), which aims to forecast diverse and physically plausible future pose sequences based on a short history of observed motion. While autoregressive sequence models have excelled in related generation tasks, their reliance on vector‐quantized tokenization limits motion fidelity and training stability. To overcome these drawbacks, we introduce \textbf{WaveAR}, a novel AR based framework which is the first successful application of a continuous autoregressive generation paradigm to HMP to our best knowledge. WaveAR consists of two stages. In the first stage, a lightweight Spatio‐Temporal VAE (ST-VAE) compresses the raw 3D-joint sequence into a downsampled latent token stream, providing a compact yet expressive foundation. In the second stage, we apply masked autoregressive prediction directly in this continuous latent space, conditioning on both unmasked latents and multi‐scale spectral cues extracted via a 2D discrete wavelet transform. A fusion module consisting of alternating cross-attention and self-attention layers adaptively fuses temporal context with low- and high-frequency wavelet subbands, and a small MLP‐based diffusion head predicts per-token noise residuals under a denoising loss. By avoiding vector quantization and integrating localized frequency information, WaveAR preserves fine‐grained motion details while maintaining fast inference speed. Extensive experiments on standard benchmarks demonstrate that our approach delivers more accurate and computationally efficient predictions than prior state‐of-the-art methods.

WaveAR: Wavelet-Aware Continuous Autoregressive Diffusion for Accurate Human Motion Prediction
Shengchuan Gao Shuo Wang"* Yabiao Wang2 Ran Yi
'Shanghai Jiao Tong University Tennent Youtu Lab "Zhejiang University gacdy111@sjo edu. en, leifwang@tencent. com caseywangetencent. com, ranyi@ajuu juu. edu. cn
Abstract
This work tackles a challenging problem: stochastic human motion prediction (SHMP), which aims to forecast diverse and physically plausible future pose sequences based on a short history of observed motion. While autoregressive sequence models have excelled in related generation tasks, their reliance on vector- quantized tokenization limits motion fidelity and training stability. To overcome these drawbacks, we introduce WaveAR, novel AR based framework which is the first successful application of continuous autoregressive generation paradigm HMP our best knowledge. WaveAR consists of two stages. In the first stage, a lightweight Spatio-Temport VAE (ST-VAE) compresses the raw 3D- joint sequence into downsampled latent token stream, providing compact yet expressive foundation. In the second stage, we apply masked autoregressive prediction directly this continuous latent space, conditioning on both unmasked latents and multi-scale spectral cues extracted via 2D discrete wavelet transform. A fusion module consisting of alternating pross-attention and self-attention layers adaptively fuses temporal context with low- and high-frequency wavelet subbands, and small Ml.e-based diffusion head predicts per-token noise residuals under a denoising loss. By avoiding vector quantization and integrating localized frequency information, WaveAR preserves fine-grained motion details while maintaining fast inference speed. Extensive experiments on standard benchmarks demonstrate that our approach delivers more accurate and computationally efficient predictions than prior state-of-fe satte-of-the-an -f-tth--rt methods.
Introduction
Human Motion Prediction (HMP) involves forecasting future human poses or motions from an observed sequence of historical poses. This capability not only deepens understanding of human behavior patterns but also underpins a wide range of applications 100 22 31
51
54 55
62
autonomous driving 23 39
robotics [16), numan-comppute interaction 25 virtual reality [13
24 30), and assisted healthcare
making HMP a rapidly advancing frontier in computer vision and artificial intelligence interaction. Early HMP methods were largely deterministic 8 28 34].
which predicted the single most likely future trajectory. While effective in some settings, these approaches neglect the inherent uncertainty and fail to capture the rich diversity of possible action sequences. Recently, the field has increasingly embraced stochastic generative frameworks such variational autoencoders (VAEs) 35 58), generative adversarial networks (G) and inffusiio-basel models 6 43). In this work, we focus on the Stochastic Human Motion Prediction (SHMP) problem that targets generating diverse yet accurate future pose sequences conditioned on the observed history.
"Equal contribution "Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

Traditional variational autoencoder ( (VAE) and generative adversarial network (GAN) methods, while effective at modeling uncertainty and diversity, can sometimes produce futures that contradict the observed history- even generate abrupt, physically implausible transitions [1]. riffusion-basee approaches yield high-fidelity trajectories but incur substantial computational overhead that hampers real-time applicability. Autoregressive (AR) modeling, in contrast, generates each future pose sequen- tially conditioned on past context, inherently preserving temporal coherence, capturing multimodal uncertainty, and producing faithful, diverse outputs. However, existing AR formulations have two key limitations: (I)Msst AR models rely on vector quantization (VQ) to discretize continuous motion se- quences into finite token sets, which introduce quantization artifacts and training instability (e.g.. codebook collapse) that degrade motion fidelity and continuity, as analyzed in recent studies [1]]6]] (2) They lack the capacity to represent fine-grained dynamics such as sudden accelerations and intricate motion transitions. These issues motivate us to design a continuous, quantization- paradigm for motion modeling. Besides, prior works have also attempted leverage frequency- domain representations- mostt commonly via the Discrete Cosine Transform (DCT)- -to capture temporal patterns, but DCT only preserves www-frquuecyy content and omits high-frequency content that captures motion details, thus impairing prediction accuracy.
AR
Recent studies have shown that images can be generated with pure autoregressive models-omitting vector quantization entirely - and in doing so suggest novel directions for AR-based generative methods[[]] [4]] In this paper, we propose WaveAR (Fig D novel AR based framework for human motion prediction that operates entirely in continuous space. This continuous formulation naturally aligns with the temporal characteristics of motion sequences, enabling the model to capture long-range dependencies more effectively and maintain stable training dynamics. Specifically, it TTmppoall Variational AutoEncoder
consists of two stages: In the first stage, we employ which simultaneously captures both spatial and temporal dependencies. This VAE can project the observed motion sequence into a smooth latent embedding in a continuous space, eliminating the quantization errors inherent in token-based schemes. In the second stage, we perform step-wise autoregressive forecasting over these latents, while simultaneously extracting low- and high-freuuency components via multiscale discrete wavelet transforms (DWT) to guide each prediction. A fusion module consisting of alternating cross-attention and self-attention layers adaptively merges the time- domain latents with their wavelet-derived counterparts, preserving the VAE's learned trajectory trends and injecting sharp, transient motion details yiieldngg future pose sequences that are both temporally coherent and richly expressive.
Our contributions can be summarized as follows:
We propose WaveAR, a novel framework for stochastic human motion prediction based continuous autoregressive paradigm that avoids quantization artifacts and effectively captures long-range temporal dependencies for accurate forecasting.
We design Masked Autoregressive Diffusion module with Wavelet Guidance, where multi- scale wavelet subbands are extracted by DWT and fused with masked future latents via alternating -otss-attention and self- layers. Afterwards, a compact MLP-baased diffusion predicts por-token noise distribution under denoising loss.
We introduce a lightweight Pentiol VAE (ST-VAE) that temporally downsamples the raw 3D-joint sequence into latent tokens, preserving joint structure while reducing sequence length for efficient downstream modeling.
We validate WaveAR on standard HMP benchmark datasets: Human3.6M 6M and HumanEva-I. Both quantitative and visualization results show that our method achieves more accurate performance with faster inference time compared to the tfate-of-the-as baselines.
2 Related Works
Human Motion Prediction. Early human motion prediction methods were predominantly deter- ministic, producing single "most likely" future trajectory. Most of these frameworks cast forecasting as a direct mapping from past poses to fixed-length future sequence, using architectures such
as RNNs [2L 36 or self-attention Transformers [ 37 -6)-nften enhanced with graph convolu- tional layers g 27 to capture spatial dependencies among joinss However, all of these solutions disregard the intrinsic uncertainty of human behavior. The advent of deep generative models has catalyzed a paradigm shift toward stochastic prediction frameworks through three principal avenues:

---

X
A1
Agg
Am
randomimask
DWT
Bell
X
Xyp
Pe
ConvED
LayerNorm
sunglin
RourecLD
Waveler-guddee Mapiced Fusson Medule
Linear
No
Rell
Xy
frequency features
diffllson-lo
toklns
tolions
fused
(a) Spatio-Teree Centio-Temmoral VAE
(b) Wavelet-guided autoregressive prediction module
Figure 1: Overall architecture of our proposed WaveAR. (a) During training, a lightweight Spatio- Temporal VAE encodes the raw 3D-joint sequence (past H future F frames) into a compact latent token stream via temporal downsampling, (b) shows the process of the wavelet-guided autoregressive masked generation model. First, the VAE latents are randomly masked, while the original input sequence's history undergoes 2D discrete wavelet transform for wavelet frequency-dome extraction, and linear projection into the same embedding space. Next, the masked latents and projected wavelet features are fused through a fusion module consisting of alternating aotsntnt and self-attention layers. Finally, compact MLP-based diffusion predictor takes the autoregressive model's s output as conditioning vector and estimates the noise residual for each token, modeling its diffusion distribution and acting as prediction head.
feature
(I) GAN-based methods 3 19
26 56 which generate diverse trajectories through adversarial training, (2) VAE-based methods 5 9
57 58) that encode motion multimodality via latent distributions and most recently (3) Diffusion based methods 22 6 7 43 [50). Among these methods, CoMusion
14
35 53
43 integrates single-stage diffusion model with Transformer reconstruction and GCN refinement DCT space for history-consister stochastic forecasting: SkeletonDiffusion
7 introduces nonisotropic Gaussian diffusion via syped-grahl convolutions with skeleton-
noise covariance; BeLFusion (2) employs conditional latent diffusion [40] by sampling disentangled
behavior codes drive motion; HumanMAC ( reframes prediction masked DCT-based diffusion
completion, jointly denoising observed and future frames for controllable, diverse outputs. Neurosci- entific studies have shown that human motion exhibits strong frequency-domain characteristics, which benefit tasks such as motion editing and motion synthesis. Accordingly, many of the aforementioned motion-predictinn approaches exploit frequency-domain representations- pitresenttiiosspprriillllry the Discrete Cosine Transform (DCT)-to model motion distributions. Motion Wavelet [12] applies wavelet manifold learning to motion prediction. Inspired by these findings, integrate wavelet-domain cues into our latent-space prediction pipeline, resulting in more accurate and temporally coherent future poses.
Autoregressive Modeling without Vector Quantization. Traditional autoregressive models for sequential data generation, such as images motion, heavily rely discrete tokenization via vector quantization (VQ) [45), which introduces quantization artifacts and training instability. Different from quctt-qquanizzttion based AR models, which represent the probability distribution of each token as a discrete multinomial distribution through a VQ codebook, MAR 22 circumvents these discrete representations' limitations by operating directly in continuous-valued spaces and representing the probability distribution of each token through a diffusion process. Such continuity avoids the loss of information during quantization, thus greatly enhances its generative quality. Building on MAR's continuous, quantization-Fii foundation, growing body of work has applied its framework across diverse domains. MARRS 49 extends MAR's ss autoregressive framework action-reaction reactioo synthesis by replacing discrete codecs with continuous representations and variational sampling. DART [S extends MAR's s diffusion-drivee autoregressive framework to a non-Markovian paradigm, enabling high-resolution image synthesis in unified Transformer sequence. MARDM 38 extends MAR's masked autoregressive diffusion framework to text-conditioned motion synthesis, restructuring motion latents via a bidirectional masked diffusion.
3

---

3 Method
3.1 Problem Formulation
Given an observed sequence of human motion with P historical poses, denoted as X [p-r+I: P- P4,, Pol. E Px333 where P. represents the 3D coordinates of J body joints at timestep t, the goal of Human Motion Prediction (HMP) is to forecast the subsequent F future poses pr] E Px333 For Stochastic Human Motion Prediction (SHMP), the objec- Y= P1: P2.
tive extends to generating N plausible future trajectories Y = (Yi, .y.,,, Yx) E RR-xxxJx3, where each Y; maintains temporal coherence while exhibiting distinct motion patterns. The key challenges in HMP include improving prediction accuracy for long-term motions, avoiding unnatural movements like sudden joint twists or behaviours that break physical laws.
3.2 Overview of WaveAR
Our method is the first framework for stochastic human motion prediction that employs an autoregres- sive model within a continuous space encoding paradigm. It consists of two stages to predict future poses from H observed frames: First, we encode the input sequence into a smooth latent embedding using a lightweight Spatio-Temporre VAE (ST-VAE). This step reduces the input dimension and creates continuous-valued tokens. In parallel, the raw 3D-joint sequence is fed into our Wavelet Feature Extractor, which applies 2D discrete wavelet transform generate four spectral subbands. These subbands help extract finer details in the motion dynamics, which are then linearly projected into the same embedding space as the latents, enabling more accurate predictions. We then recover the F future tokens with our Masked Autoregressive Diffuser: at each iteration, a subset of tokens is masked, and the Wavelet guided fusion module fuses spectral cues (keys/values) with token queries to get latent motion information. It is used to denoise the masked positions through several MI.P layers together with diffusion loss. After all tokens are restored, a Latent Decoder reconstructs the full 3D joint trajectories. The overall structure of our network is shown in Fig. 1
3.3 Spatio-Temporal VAE
To eliminate the dependency of prior autoregressive approaches on discrete VQ-VAE codes, we employ a lightweight Spapoolll VAE (ST-VAE) that produces fully continuous latent to- kens, simultaneously obtaining a vector representation that integrates both temporal and spatial information.. In the first stage, we use this ST-VAE (see Fig. [a] to compress the raw 3D-joint comprion H past frames and F' future frames-ine into a shorter sequence of continuous latents. Specifically, given mini-batch X E RBXTXC where T' is the sum of the historical and predicted frames, and C is the number of joints multiplied by (representing the 3D position vector at each time step), reshape it to B C' x T and pass it through the encoder, which consists of ID convolution, several ResNet1D blocks [59], whose role is to integrate spatial information, and strided convolutions that downsample time by a factor to effectively integrate temporal information. The encoder then outputs and a for each latent token, and we sample via the reparameterization trick:
@xxree,where
z = (x() - (X) Oe. E 6~ 100,
(I)
where دديي and I:LL output of the encoder. To get the reconstructed original sequence X, Z is first passed through a post-quantization and then through the Decoder. The ST-VAE is trained to minimize the following loss:
~vAE= X 1-X-X/11 B ()),
(2)
where 3 balances reconstruction fidelity against latent regularization. By temporally downsampling the input token, the ST-VAE produces sequence of latent vectors that both compress redundant frames and encode the underlying motion dynamics [[7], yielding a compact token stream that supports more efficient and accurate downstream prediction.
3.4 Autoregressive Masked Generation with Wavelet Guidance
Wavelet Feature Extraction. Prior motion prediction works typically employ the Discrete Cosine Transform (DCT) for frequency-domain processing of motion. While DCT captures the low-frequency

---

motion trends, it overlooks high-frequency dynamics critical for subtle or rapid movements. To avoid this limitation, we employ the Discrete Wavelet Transform (DWT), which yields both low- and high- frequency subbands by contrast, furnishing richer spectral cues and improving prediction accuracy. To inject these spectral cues into our autoregressive diffusion backbone, we apply vanilla DWT operation to the raw 3D-joint history sequence along both temporal and channel axes. The transform uses pair of ē-length filters: a low-pass filter and a high-pass filter derived from a chosen discrete wavelet basis such as Harr. Specifically, given motion sequence æli, j] (i E [1, H + F), j E [1, 3//)) and a, b E {L, H3. we compute four subbands (YL,L, YI,H. YH,L, HH.H) ) as follows:
H+P 3J
Ya, K2) = ΣE 2k1) fo 2k2) æ|i,j},
(3)
==
where fI and fH represent the low-pass and high-pass filters, respectively. Here, each subband 2 3++6+1 kn E [], h2 E [1, DJ. Concretely, Yt.1 captures the low-frequency coefficients, YI.H, Yn.I. capture the temporal- or spatial-detail detail in both axes. Then we concatenate the four
Ya,s E BRXD K -+ D
coefficients, and Yи.и captures high-frequene
subbands along the channel axis to get Y = [V,.,, YL.H, YH.L, Yн.h) E Rxx44 following the
approach [2]. Finally, we use a learned linear projection to map these 4D-dimensional features into the same d-dimensional latent space as the ST-VAE tokens as follows:
Faave b| (N Y + +b E R
where LN denotes the layer normalization and W is the learned linear matrix.
By explicitly computing both approximation and detail coefficients, the processed wavelet subbands provide the later Transformer fusion block with direct access to multi-scale, time-frequency motion patterns- centelin-naallngg. more precise recovery of masked future tokens that better align with the input historical information.
Wavelet-guided Masked Fusion Module. To organically fuse spectral cues with mask embeddings for more precise modeling of the original motion, during training, the original motion input first processed by ST-VAE to produce continuous-valued tokens. We first mask out a random subset of the tokens with learnable [MASK] embedding to obtain Xmakked E R xd We then add shared
positional encoding Epss E 7 to form the initial token stream.
In parallel, the wavelet branch produces frequency-domait features Fwave E Rxd, We then design fused Transformer to learn interactions between frequency-doman features and time-domain motion tokens. Specifically, we partition the Transformer stack into two phases-eat local fusion with cross-attention, followed by later global aggregation via self-attention. Transformer blocks are local fusion layers, each consisting of cross-attention sublayer followed immediately by self-attention sublayer. time-domain tokens with frequency cues via cross-attention before letting them attend globally to one another. 2) After the Niocal local fusion layers, the remaining as global context layers, each containing only standard self-attention sublayer plus feed-forward network-no further cross-attention to the wavelet branch. This mechanism enables the model to first inject complementary frequency information into the masked latents, and then to refine the joint representation through additional layers of purely temporal self-ateention before projecting back to the diffusion MLPs. Specially, for local fusion layers, we first calculate the fusion of frequency features and latent features:
The first Nioca
layer l = = l..... .Noocal, these local layers tightly fuse
Niocal Transformer blocks act
(QW2) (KWK)" Vdz (X'Wa) (X WT (x'WV'),
x' CrossAtt (Q, K, V) softmax
(VWv),
(5)
X Xout SelfAunn K, V) = softmax
Vdl
where Wq, W key vector K. Thee query Q comes from the latent representations, while the keys K and values V are derived from the frequency-dome features. For the rest layers, the los layer's 's result is calculated as follows:
q. Wx.WW, Wv are trainable weight matrices. de is the dimensionality the
(X"=))W) (X (xx' ((U'"W>>")) (xxI-11 (6)
x = SelfAutn(Q. K, V) = softmax
Vdk
5

---

where (1=1)) is the output of previous layer.
Diffusion for Autoregressive Prediction. As shown MAR 29 relying solely on autoregressive model (without diffusion prediction head) cannot capture chained token dependencies, resulting in poor generation quality. Inspired by its success image synthesis, we incorporate the MAR diffusion loss into our human motion prediction framework. Given the fused representation X of masked latents and wavelet embeddings, we perform autoregressive diffusion to reconstruct the original motion tokens. Instead modeling the full joint distribution in a single pass, we generate each token conditioned on all previously generated tokens, following the continuous-valuee MAR paradigm adapted motion data:
N p(.1....,, IN) p( (1=-1),
where {2i is the sequence of tokens, and the index runs from to n, specifying their order.
We use our wavelet-guided fused Transformer to produce, for each position i, a conditioning vector
2s f( 1 DWT) RRd for the diffusion network in MAR. To learn p(r;; =x) in continuous space, we corrupt the ground-truth token I; under the standard DDPM schedule and train a noise predictor to recover the added noise via a diffusion loss objective:
(t) F σe Fi (1 - az) N~0 I), t= T.
(8)
Laitt Ei,,. e Eo(x)"
(1) 14, 21,
where is noise schedule indexed by time step and (1: (t) |t, 2x) denotes the neural network's prediction of the noise, taking z/ as input and being conditioned on both the time / and the conditioning variable 2₁. We implement Eg as three-block MLP with residual connections and LayerNorm.
3.5 Inference
At test time, first encode the observed history X1:I H3J with the ST-VAE encoder obtain continuous latents X 1-H Enc(x1:H) We then form full-length token sequence ( RR F) by setting its first H rows to Z1:H and masking
future positions with a learnable [MASK] embedding. Over K autoregressive iterations, autoregressive step & compute the unmasking
ratio Pk cos k 2K
and select F' of the still-masked tokens to predict in this round. We feed the
current u (t-1) together with the precomputed wavelet embeddings DWT((x::11 into the diffusion network fo- Only the newly unmasked positions i are updated by denoising sampler at diffusion time step
u
(A)
1 [(
(k-1)
az - āt
Ep (u. (1-11 11, 21) + ofe,
e e~ N~000,
(10)
Voe
VI
where & 1...., K indexes the autoregressive iterations, and t = 1,..., T indexes the diffusion timestep. Here, ay, and are the diffusion noise schedule parameters at diffusion step t, and co(- t,2) is the noise predictor conditioned on latents z. while other tokens remain fixed. After (T) T rounds, every future token has been filled in. Finally, we extract # and decode it via the ST-VAE decoder to get the final prediction result:
(T) *H+1-H+F Dec u #
Fx3J.
(11)
4 Experiments
4.1 Experimental Settings
Datasets. We evaluate our method on two widely adopted benchmarks for stochastic human motion prediction (SHMP): Human3.6M (20). HumanEva-1 [42] and AMASS 333. Human3.6M is a large- scale benchmark containing 3.6 million frames of 3D human joint positions captured Hz, featuring seven actors performing 15 diverse activities (c.g., walking, cating, and discussing). To

---

Table I: Quantitative comparison on HumanEva-I and Human3.6M.
HumanEva-1
Human3.6M
Method
APDT
ADE1
FDEL
MMADEL
MMFDEL
APDT
ADE1
FDEL
MMADEL
MMFDEJ
DeLEGAN 19
2.177
0.306
0.385
0.371
0.520
0.545
DSF 5x
4,538
0.273
0.290
0.364
0.340
9.330
0.493
0.592
0.550
0.599
2.846
0.351
0.514
DLow 53
4,855
0.251
0.268
0.362
0.339
11.741
0.425
0.518
0.495
0.531
GSPS
5.825
0.331
14.757
0.476
DivSamp g
6.109
0.220
0.234
0,342
0,316
15,310
0.570
0.485
0.475
0.516
STARS
6.031
Mition 50
5.931
0.232
0.236
0.352
0.320
15,353
0.411
0.509
0.508
0.536
7,602
0.473
HumanMAC 6
6.554
0.209
0,223
0.342
0.320
6.301
0.369
0,480
0.509
0.545
CoMusion
0.494
0.506
3.128
0.354
0.337
0.513
0.535
ensure compatibility with prior studies, we follow the protocol of 6 49 modeling each pose with 16-joint skeleton. Given the first 0.5 seconds (25 frames) of observed motion, the task is to forecast the subsequent seconds (100 frames). HumanFva-I provides 3D motion captured at 60 Hz from three actors each performing five distinct movements, with poses encoded as 15--oiit skeletons. Following common practice, we use the first 0.25 s (15 frames) of each sequence as input and task our model with forecasting the next I s (60 frames) of motion. Results AMASS dataset can be seen in supplementary materials.
Baselines. We compare our method against eleven representative baselines, covering VAB-based, GAN-based, and diffusion-based approaches: DeLiGAN, DSF, BoM, DLow, GSPS, DivSamp, STARS, MotionDiff, Bel. HumanMAC, and CoMusion. Belfusion and Comusion didn't t perform experiments on HumanEva-I datases
Metrics. We evaluate our model using five established metrics for stochastic human motion pre- diction. The Averuge Puirwise Distance (APD) quantifies diversity by computing the mean L2 distance between all generated motion samples. Averuge Displacement Error (ADE) measures overall sequence accuracy as the minimum average 1.2 distance between predictions and the ground truth, while Final Displacement Error ( (FDE) focuses precision at the final predicted frame. To address multi-modal scenarios, Multi-Modal ADE (MMADE) and Multi-Modal FDE (MMFDE) extend these metrics by grouping ground truth sequences based on similar initial observations, ensuring robust evaluation plausible diverse outcomes. These metrics collectively assess accuracy, temporal consistency, and diversity, critical for real-world applications requiring both precision and variability.
4.2 Implementation Details
We employ lightweight VAE with a two-layer encoder-decoder architecture. Each hidden layer has a dimension of 128, and we apply temporal downsampling rate of 2. It is trained for 500 epochs with a batch size of 128. Spectral cues are injected by applying a vanilla Haar wavelet transform to the raw 3D-joint history. For the Human3366 dataset, the diffusion backbone consists of 12 Transformer layers: the first layers each combine self-attention and cross-attention over the wavelet embeddings, while the remaining 6 layers use only self-attention. We set the latent dimension to 256. For HumanEva-l, we use the same overall design but employ only 3 layers with both self- and cross-attention, followed by 3 self-attention layers, also with latent dimension of 256. The noise prediction network in the diffusion model is a 3-layer MLP with a hidden dimension optimize the model for 200 epochs using the Adam W optimizer (32 with 31 0.5, 82 0.99, and an initial learning rate of 2 x 10-4, A multi-step learning-rate scheduler with decay factor 7 = 0.9 is applied, and the batch size is increased 256 stabilize training.
1024. We
4.3 Comparison with the State-of-the-Arts
Quantitative Comparison. Table [reports quantitative results on HumanEva-1 and Human3.66 3.6M, comparing our method against recent baselines. It can be seen that our approach achieves the best ADE and FDE values on both the HumanEva-I and Human3.6M datasets, indicating that our method provides the most accurate predictions. While method yields lower APD, note that many
7

---

Observation 6.s
Ours
HumanMAC
Figure 2: Qualitative comparisons: The first line is input history and ground truth motion, both methods predict ten predictions based on the same input history.
high-diversity models tend to sacrifice accuracy by generating trajectories that deviate from the observed history or true motions. These methods prioritize variety the expense of fidelity. In contrast, our approach deliberately emphasizes producing the most plausible sequences, resulting in highly accurate predictions with more concentrated samples. As consequence, although our method shows lower diversity, also reflected in the slightly worse MMADE and MMFDE scores compared to some competitors, it maintains stronger adherence to the dominant motion modes. This highlights the trade-off between diversity and accuracy, with our model prioritizing precision while still ensuring appropriate level of diversity.
Qualitative Comparison. To further illustrate the effectiveness of our approach, Figure 4 presents qualitative predictions two different actions. In each sequence, model faithfully reproduces the nuanced joint trajectories of the ground truth, accurately capturing both smooth cyclic motions predictions exhibit superior stability: in the first
(e.g., walking) and rapid transitions. Morcover, example, the HumanMAC baseline produces an abrupt motion jump at the end of its forecast, whereas model maintains coherent and physically plausible dynamics throughout. These visualizations demonstrate that our continuous-latent wavelet-condition diffusion framework not only excels in numerical accuracy but also delivers compellingly realistic motion prediction.
Table 2: Comparison of inference time of different model sizes.
Model
Params (M)
Inference Time (s)
APD1
ADE|
FDEL
HumanMAC
1.25
6.301
0.369
0.480
WaveAR (tiny)
26.8
0.21
4.952
0.354
0.465
WaveAR (small)
51.9
0.43
4.458
0.347
0.452
WaveAR (base)
86.5
0.65
4.633
0.342
0.450
Efficiency and Computational Analysis To demonstrate the efficiency and scalability WaveAR, we provide three variants of our model with approximately 27M, 52M, and 87M parameters, re- spectively. We observe that enlarging the model size improves prediction accuracy but comes cost of reduced diversity. We provide comparison of parameters and inference time for WaveAR and HumanMAC. The results are shown in Tat2] Thanks to the reduced number of denoising steps, WaveAR performs inference substantially faster than HumanMAC, while maintaining high prediction accuracy and realistic motion quality.
4.4 Motion In-Betweening via Flexible Masking
Beyond pure forecasting, our flexible masking strategy enables natural motion in in-betweening between two distinct action sequences. By masking out an intermediate span latent tokens and unmasking them progressively. the model seamlessly "fills in" transition that respects both the initial and target poses. This adaptability arises from our autoregressive diffusion framework's ability treat any masked -oarval-hetterr at the end of sequence in the middle-as generation problem

---

0.0s
0,46
0.85
1.25 1.65 2.0s
Sitting to standing
Standing to sitting
Turn around
Figure 3: Motion in-betweening results of our proposed WaveAR model on the Human3 6MM dataset. The first two columns represent the given initial motion, and the last two columns represent the target motion to be transitioned to. The visualization demonstrates that our model smoothly transitions from one motion to another. Both the initial and target motions consist of 20 frames.
Table 3: Ablation studies proposed components, conducted the Human3.6M dataset. Here, "w/o ST ST-V indicates applying the downstream model directly on the raw input space; "w/ ST- VAE (y 1)" denotes ST-VAE with a single downsampling (our implementation uses two
downsamplings): "w/o DWT" removes the DWT branch.
Method
APD
ADEL
FDEI
MMADE
MMFDE!
w/o ST-VAE
8.625
0.492
0.641
0.570
0.692
w/o DWT
4.781
0.381
0.503
0,535
0.549
w/ ST-VAE (7 1)
5.263
0.446
0.586
0.564
0.597
Ours
4.458
0.347
0.451
0.513
0.535
conditioned on its surrounding context. The visualization results are presented in Figure 3 Our model smoothly transitions between motions. Each joint follows realistic physical constraints, ensuring the coherence and plausibility of the generated in in-between frames. Not only can it handle simple motion transitions, such as turning, but can also manage more complex sequences involving multiple action changes. For example, in the first row, the model successfully generates a sequence that transitions from sitting posture to standing, followed by a turn and an exit motion. This demonstrates model's 's ability to capture and generate dynamic transitions across varied motion types.
4.5 Ablation Study
In this section, we perform an ablation analysis on the Human3.66 6.M dataset to investigate the impact of various design choices in model, examining how each component contributes to its motion forecasting performance. Specifically, we conduct ablation experiments on the proposed frequency- domain module, the VAE module, the of continuous latent vectors, and the diffusion settings. These experiments help us understand the individual contributions each module and design choice, highlighting their influence on the overall performance of the model.
Network Architecture Component. We first conduct network component ablation experiments evaluate the roles of different submodules in our architecture. The results are shown in Table 3
Diffusion for Autoregressive Prediction. We next explore the contributions of diffusion-relate components for our autoregressive prediction framework. First, we replace our continuous ST- VAE tokens with discrete codes from VQ-VAE-thi substitution leads to marked degradation in both ADE and FDE, as shown in Table Second, we remove the diffusion process entirely and instead use the output of the masked Transformer directly to reconstruct the original motion sequence via an La loss; this baseline also performs poorly, confirming that the diffusion mechanism is essential for accurate, smooth motion generation.

---

Wavelet-based Frequency Module We further analyze the impact of the proposed Discrete Wavelet Transform (DWT) module, which replaces the conventional Discrete Cosine Transform (DCT) used in prior frequency-based motion models. As shown in Table 5, introducing the DWT consistently improves motion prediction accuracy compared with both the baseline without any frequency module and the DCT-based variant. This demonstrates that the adaptive representation of wavelets better aligns with the temporal characteristics of motion sequences and provides more precise predictions.
Table 4: Performance comparison of different loss functions.
Loss
APD
ADE
FDE 4
MMADE
MMFDE
VQ-VAE
7.233
0,477
0.589
0.544
0.631
L2 Loss
5,330
0.422
0.548
0.539
0.581
Ours
4.458
0.347
0.451
0.513
0.535
Table 5: Ablation study on different frequency-domain designs.
Method
APD f
ADE 4
FDE
MMADE 4
MMFDE +
w/o DWT & DCT
4.781
0.381
0.503
0.535
0.549
w/ DCT
5.016
0.362
0.478
0.521
0.537
DWT (Ours)
4.458
0.347
0.451
0.513
0.535
Conclusion
In this paper, we propose WaveAR, novel AR based framework, which the first successful application of continuous autoregressive generation paradigm to human motion prediction. Unlike prior methods that rely on discrete VQ-VAE codes, WaveAR employs lightweight Spatio-Temporal VAE to encode raw 3D-joint sequences into smooth, quantization-free tareateioo-eeeeeeeeeee latents. Complementary spectral cues are injected via 2D discrete wavelet transform and fused into the latent stream through local cross-attention layers, after which purely temporal self-attention layers refine the joint representation. A masked autoregressive diffusion process then generates each new token conditioned on its predecessors and rich wavelet features, yielding highly accurate, physically plausible motion forecasts. Extensive experiments on HumanEva-l and Human3.6X, show that WaveAR achieves the lowest ADE and FDE among all baselines, confirming its superior precision.
6 Limitations
Our masked autoregressive diffusion module is primarily optimized to minimize reconstruction emro the average trajectory, which lead conservative "safe" predictions, with diversity confined to a narrow range of motion variations.FP BeaatisnnsBesidess, our current framework relies on standard joint-coordinate datasets containing only about 17-32 keypoints, which restricts its ability to capture subtle or fine-grained body movements. Addressing these limitations will be an important direction for our future work.
10

---

Acknowledgements
This work was supported by National Natural Science Foundation of China (No. 62302297), the Fun- damental Research Funds for the Central Universities (YG2023QNB17, YG2024QNA44), National National Natural Science Foundation of China
Key R&D Program of China (2024YF0011550) (No, 72192821, 62272447, 62472282, 62472285), Young Elite Scientists Sponsorship Program by CAST (2022QNRC001) Beijing Natural Science Foundation (L222117). We thank our project lead, Yabiao Wang, for his guidance.
References
[1] Emre Aksan, Manuel Kaufmann, Peng Cao, and Otmar Hilliges. A spatio-temporal transformer for 3d human motion prediction. In 2021 International Conference on 3D) Vision (3DV), pages 565-574. JEEE, 2021.
[2] German Barquero, Sergio Escalera, and Cristina Palmero. Belfusion: Latent diffusion for behavior-driven human motion prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2317-2327, 2023.
[3] Emad Barsoum, John Kender, and Zicheng Liu. Hp-gan: Probabilistic 3d human motion prediction via gan. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pages 1418-1427, 2018.
[4] Apratim Bhattacharyya, Bernt Schiele, and Mario Fritz. Accurate and diverse sampling of sequences based on "best of many" sample objective. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8485-8493, 2018
[5] Yujun Cai, Yiwei Wang, Yiheng Zhu, Tat-Jen Cham, Jianfei Cai, Junsong Yuan, Jun Liu, Chuanxia Zheng. Sijie Yan, Henghui Ding, et al. A unified 3d human motion synthesis model via conditional variational auto-encoder. In Proceedings of the IEEE/CVF International Conference Computer Vision, pages 1 11645-11655 2021.
[6] Ling-Hao Chen, Jiawei Zhang, Yewen Li, Yiren Pang, Xiaobo Xia, and Tongliang Liu, Hu- manmac: Masked motion completion for human motion prediction. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9544-9555, 2023.
[7] Cecilia Curreli, Dominik Muhle, Abhishek Saroha, Zhenzhang Ye, Riccardo Marin, and Daniel Cremers. Nonisotropic gaussian diffusion for realistic 3d human motion prediction. arXiv preprint arXiv:2501.066 2025.
[8] 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, pages 11467-1 1476, 2021.
[9] 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 multimedia, pages 5162-5171, 2022.
[10] Ke Fan, Junshu Tang, Weijian Cao, Ran Yi, Moran Li, Jingyu Gong, Jiangning Zhang, Yabiao Wang, Chengjie Wang, and Lizhuang Ma. Freemotion: A unified framework for number-free text-to-motion synthesis. In European Conference on Computer Vision, pages 93-109. Springer, 2024.
[11] Ke Fan, Shunlin Lu, Minyue Dai, Runyi Yu, Lixing Xiao, Zhiyang Dou, Junting Dong, Lizhuang Ma, and Jingbo Wang. Go to zero: Towards zero-shot motion generation with million-scale data. arXiv preprint arXiv:2507.070000 2025.
[12] Yuming Feng, Zhiyang Dou, Ling-Hao Hao Chen, Yuan Liu, Tianyu Li, Jingbo Wang, Zeyu Cao, Wenping Wang. Taku Komura, and Lingjie Liu. Motionwavelet: Human motion prediction via wavelet manifold learning, arXiv preprint arXiv:2411.1696. 2024.

---

[13] Nisal Menuka Gamage, Deepana Ishtaweera, Martin Weigel, and Anusha Withana. So pre- dictable! continuous 3d hand trajectory prediction in virtual reality. In The 34th Annual ACM Symposium on User Interface Software and Technology, pages 332-343, 2021.
[14] Chunzhi Gu, Jun Yu, and Chao Zhang. Learning disentangled representations for controllable human motion prediction. Patlern Recognition, 146:109998, 2024.
[15] Jiatao Gu, Yuyang Wang, Yizhe Zhang, Qihang Zhang, Dinghuai Zhang, Navdeep Jaitly, Josh Susskind, and Shuangfei Zhai. Dart: Denoising autoregressive transformer for scalable text-to-image generation. arXiv preprint arXiv:2410.08159, 2024.
[16] Liang-Yan Gui, Kevin Zhang, Yu-Xiong Wang. Xiaodan Liang, José MF Moura, and Manuela Veloso. Teaching robots to predict human motion. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 562-567. IEEE, 2018.
[17] Chuan Guo, Shihao Zou, Xinxin Zuo, Sen Wang. Wei Ji, Xingyu Li, and Li Cheng. Generating diverse and natural 3d human motions from text. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5152-5161, 2022.
[18] Chuan Guo, Yuxuan MU, Muhammad Gohar Javed, Sen Wang, and Li Cheng. Momask: Generative masked modeling of 3d human motions. In Proceedings of the IEEE/CVP Conference on Computer Vision and Pattern Recognition, pages 1900-1910, 2024.
[19] Swaminathan Gurumurthy, Ravi Kiran Sarvadevabhatla, and R Venkatesh Babu. Deligan: Generative adversarial networks for diverse and limited data. In Proceedings of the IEEE conference computer vision and pattern recognition, pages 166-174, 2017.
[20] Catalin Ionescu, Dragos Papava, Vlad Olaru, and Cristian Sminchisescu. Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence, 1325-133,, 2013.
[21] Ashesh Jain, Amir R Zamir, Silvio Savarese, and Ashutosh Saxena. Structural-rnn Deep learning spatio-temporal graphs. In Proceedings of the ieee conference computer vision and pattern recognition, pages 5308-5317, 2016.
[22] Xuan Ju, Ailing Zeng, Jianan Wang, Qiang Xu, and Lei Zhang. Human-art: A versatile human-centric dataset bridging natural and artificial scenes. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 618-629, 2023.
[23] Kyungdo Kim, Yoon Kyung Lee, Hyemin Ahn. Sowon Hahn, and Songhwai Oh. Pedestrian intention prediction for autonomous driving using a multiple stakcholder perspective model. In 2020 JEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7957-7962. IEEE, 2020.
[24] Kyungdo Kim, Yoon Kyung Lee, Hyemin Ahn. Sowon Hahn, and Songhwai Oh. Pedestrian intention prediction for autonomous driving using a multiple stakeholder perspective model. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7957-7962. IEEE, 2020.
[25] HS Koppula and A Saxena. Anticipating human activities for reactive robotic response [c]. In IROS, 2013.
[26) Jogendra Nath Kundu, Maharshi Gor, and R Venkatesh Babu. Bihmp-gan: Bidirectional 3d human motion prediction gan. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 8560- 2019.
[27] Maosen Li, Siheng Chen, Zihui Liu, Zijing Zhang, Lingxi Xie, Qi Tian, and Ya Zhang. Skeleton graph scattering networks for 3d skeleton-based human motion prediction. In Proceedings of the IEEE/VE international conference on computer vision, pages 854-864 2021.
[28] Maosen Li, Siheng Chen, Zijing Zhang, Lingxi Xie, Qi Tian, and Ya Zhang. Skeleton-parte parted graph scattering networks for 3d human motion prediction. In European conference on computer vision, pages 18-36. Springer. 2022.
12

---

[29] Tianhong Li, Yonglong Tian, He Li, Mingyang Deng, and Kaiming He. Autoregressive image generation without vector quantization. Advances in Neural Information Processing Systems, 37:56424-56445, 2024.
[30] Fengqi Liu, Hexiang Wang, Jingyu Gong, Ran Yi, Qianyu Zhou, Xuequan Lu, Jiangbo Lu, and Lizhuang Ma. Emphasizing semantic consistency of salient posture for speech-driven gesture generation. In Proceedings of the 32nd ACM International Conference on Multimedia, pages 7027-7035, 2024.
[31] Shuijing Liu. Peixin Chang, Zhe Huang, Neeloy Chakraborty, Kaiwen Hong, Weihang Liang, Livingston McPherson, Junyi Geng, and Katherine Driggs-Campber Intention aware robot crowd navigation with attention-based interaction graph. In 2023 IEEE international conference on robotics and automation (ICRA), pages 12015-12021. IEEE, 2023.
[32] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.
[33] Naureen Mahmood, Nima Ghorbani, Nikolaus F Troje, Gerard Pons-Moll, and Michael J Black. Amass: Archive of motion capture surface shapes. In Proceedings of the IEEE/CVF international conference on computer vision, pages 5442-5451, 2019.
[34] Wei Mao, Miaomiao Liu, and Mathieu Salzmann. History repeats itself: Human motion prediction via motion attention. In Computer Vision-ECC ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Purt XIV 16, pages 4744889. Springer, 2020.
[35] Wei Mao, Miaomiao Liu, and Mathieu Salzmann, Generating smooth pose sequences for diverse human motion prediction. Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13309-13318, 2021.
[36] Julieta Martinez, Michael J Black, and Javier Romero. On human motion prediction using recurrent neural networks. In Proceedings of the IEEE conference on computer vision and pattem recognition, pages 2891-2900, 2017.
[37] Angel Martinez-Gonzáler Michael Villamizar, and Jean-Mare Odobez. Pose transformers (potr): Human motion prediction with non non-autoregressive transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2276 7284,224, 2021.
[38] Zichong Meng, Yiming Xie, Xiaogang Peng, Zeyu Han, and Huaizu Jiang. Rethinking diffusion for text-driven human motion generation. arXiv preprint
2024.
[39] Brian Paden, Michal Čáp, Sze Zheng Yong, Dmitry Yershov, and Emilio Frazzoli. survey of motion planning and control techniques for self-driving urban vehicles. IEEE Transactions on intelligent vehicles, 1(1):33-55, 2016.
[40] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björ Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 10684-10695, 2022.
[41] Tim Salzmann, Marco Pavone, and Markus Ryll. Motron: Multimodal probabilistic human motion forecasting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6457-6466, 2022.
[42] Leonid Sigal, Alexandru O Balan, and Michael J Black. Humaneva: Synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. International journal of computer vision, 87(1):4-27, 2010.
[43] Jiarui Sun and Girish Chowdhary. Comusion: Towards consistent stochastic human motion prediction via motion
material-. In European Conference Computer
Vision, volume 2, page 5, 2024.
[44] William Taylor, Syed Aziz Shah, Kia Dashtipour, Adnan Zahid, Qammer H Abbasi, and Muhammad Ali Imran. An intelligent non-invasive real-time human activity recognition system for next-generation healthcare. Sensors, 20(9):2653,
13

---

[45] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems. 30, 2017.
[46] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
[47] 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, pages 3332-3341, 2017.
[48] Yabiao Wang, Shuo Wang, Jiangning Zhang, Ke Fan, Jiafu Wu, Zhucun Xue, and Yong Liu. Timotion: Temporal and interactive framework for efficient numan-human motion generation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 7169-7178, 2025.
[49] YB Wang, Shuo Wang. JN Zhang, JF Wu, QD He, Fu, CJ Wang, and Yong Liu, Marrs: Masked autoregressive unit-based reaction synthesis. arXiv preprint arXiv:2505. 11344, 2025,
[50] Dong Wei, Huaijiang Sun, Bin Li, Jianfeng Lu, Weiqing Li, Xiaoning Sun, and Shengxiang Hu. Human joint kinematics diffusion-refinemmntt for stochastic motion prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 61 10-61 18, 2023.
[51] Hao Wen, Hongbo Kang, Jian Ma. Jing Huang, Yuanwang Yang. Haozhe Lin, Yu-Kun Lai, and Kun Li. Dycrowd: Towards dynamic crowd reconstruction from a large-scene video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
[52] Sirui Xu, Yu-Xiong Wang, and Liang- Liang-Yan Gui. Diverse human motion prediction guided by multi-level spatial-temporal anchors. In European Conference on Computer Vision, pages 251-269. Springer, 2022.
[53] Xinchen Yan, Akash Rastogi, Ruben Villegas, Kalyan Sunkavalli, Eli Shechtman, Sunil Hadap, Ersin Yumer, and Honglak Lee. Mt-vae: Learning motion transformations to generate mul- timodal human dynamics. In Proceedings of the European conference on computer vision (ECCV), pages 265-281, 2018
[54] Jie Yang, Ailing Zeng, Feng Li, Shilong Liu, Ruimao Zhang, and Lei Zhang, Neural interactive keypoint detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15132, 2023.
[55] Ran Yi, Yong-Jin Liu, Yu-Kun Lai, and Paul L Rosin. Quality metric guided portrait line drawing generation from unpaired training data. IEEE Transactions on Pattern Analysis and Machine Intelligence (5(1):905-918 2022.
[56] Ran Yi, Teng Hu, Mengfei Xia, Yizhe Tang, and Yong-Jin Liu. Feditnet++: Few-shot editing of latent semantics in gan spaces with correlated attribute disentanglement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
[57] Ye Yuan and Kris Kitani. Diverse trajectory forecasting with determinantal point processes. arXiv preprint 1XX.:1907 2019.
[58] Ye Yuan and Kris Kitani. Dlow: Diversifying latent flows for diverse human motion prediction. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IX 16, pages 346-364. Springer, 2020.
[59] Jianrong Zhang, Yangsong Zhang, Xiaodong Cun, Yong Zhang, Hongwei Zhao, Hongtao Lu, Xi Shen, and Ying Shan. Generating human motion from textual descriptions with discrete representations. In Proceedings of the IEFF/CVF conference on computer vision and pattern recognition, pages 14730-14740, 2023.
[60] Yan Zhang, Michael J Black, and Siyu Tang. We are more than our joints: Predicting how 3d bodies move. Proceedings of the IEEF/CVE Conference on Computer Vision and Pattern Recognition, pages 3372-3382, 2021.
14

---

[61] Chuanxia Zheng and Andrea Vedaldi. Online clustered codebook. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2798-22807, 2023.
[62] Jieming Zhu, Quanyu Dai, Liangcai Su, Rong Ma Jinyang Liu, Guohao Cai, Xi Xiao, and Rui Zhang. Bars: Towards open benchmarking for recommender systems. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2912-2923, 2022.
15

---

Supplementary Materials
A Per-Class Performance Comparison on Human3.6M
We conduct extensive experiments on the Human3.6A 6M dataset, which encompasses 13 diverse action categories including Directions, Discussion, Eating, Greeting, Phoning, Photo, Posing, Purchases, Sitting, SittingDown, Smoking, Waiting, Walking, WalkDog, and TalkTogether. presens detailed comparison of method against several state-of-the-ard approaches across all action categories, From Table 66, it can be observed that our method demonstrates strong performance in terms of both ADE and FDE across wide range of action categories. In particular, our approach achieves significant improvements on two walking-related actions Walking and WalkTogether where precise motion prediction is crucial. This indicates the model's enhanced ability capture and forecast dynamic motion patterns, especially in scenarios involving coordinated or continuous movement.
B Wavelet guided masked fusion module
The detailed structure of our proposed Wavelet-guide masked fusion module is shown in Fig 4 The module consists of a hierarchical arrangement of two distinct layer types. The lower section comprises N local fusion layers, each built with a sophisticated architecture incorporating CrossAttn, SelfAttn, and FFN components. Within these local fusion layers, the cross-attention mechanism facilitates information exchange between frequency domain features and masked vectors in the latent space, enabling effective N global context layers, constructed exclusively with self-attention mechanisms and feed-forward networks. These layers are specifically designed to consolidate global information, progressively refining representations to produce condition vector that encapsulates comprehensive contextual understanding. our implementation, we set N = 6, creating a balanced architecture with sufficient capacity for both local feature fusion and global context integration.
integration a fine-grained level. The upper section contains
condition
*N global context layer
FFN
FFN A SelfAttn
global context layer
SelfAttn
FFN
Local fusion layer
CrossAttn
Local fusion layer
Xmask
frequency features
Figure 4: The detailed architecture of the Wavelet guided masked fusion module
C More ablation study
In this part, we conduct additional ablation studies further analyze the effectiveness of key components in our method. Specifically, we examine: (1) the impact of varying the number of layers in the masked fusion module, and (2) the design of the denoising steps within the diffusion process.For For the first ablation study, we investigate how different configurations of local fusion layers and total layers affect model performance. As shown in Table 7 we systematically vary the number of local fusion layers (ranging from 4 to 12) while adjusting the total number of layers (ranging from to 14). The results demonstrate that a proper configuration of these architectural components significantly impacts model performance. Specifically, the model achieves optimal performance on our task when using local fusion layers within total of 12 layers, yielding the best overall performance. For the second ablation study, we examine different noise schedule configurations on
16

---

Classes
APD ADE
FDE
MMADE
MMFDE
Classes
APD ADE FDB
MMADE
MMFDE
TPK
6.510
0.447
0.482
0.523
0.544
TPK
6,417 0,400 0.547
0,461
0.548
DLow
11.874 0.415
0.465
0.499
0,514
DLow
11.425 0.364 0,513
0.440
0.523
GSPS
15.398 0.407
0.522
GSPS
14.966 0.323 0.454
0,411
0.484
Directions
BelFusion
DivSamp
15,663 0,389 0.463
7.090
0.378
0.502
0.523
0.494
Sitting
BelFusion
DivSamp
15.614 0.317 0.465
6.495 0.306 0.446
0,400
0.417
0.490
0.461
HumanMAC
6.357
0.391 0.456
0.475
0.475
HumanMAC
5,941 0.312 0.456
0.404
0.472
CoMusion
7.527
0.372 0.417
0,454
0.435
CoMusion
0.307 0.448
0,406
0.468
Ours
4.673 0.371 0,421
0,467
0.461
Ours
4,080 0.302 0.443
0,437
0.492
TPK
6.966 0.511 0.581
0.570
0,600
TPK
7.393 0.496 0.678
0.531
0.666
DLow
11.872
0,472 0,536
0.533
0.549
DLow
12.044 0,451 0.605
0,495
0.606
GSPS
14.099 0.448 0.541
0.526
0.563
GSPS
13.725 0.406 0,561
0.461
0.565
Discussion
DivSamp
15.310
0,432 0.526
0.534
0.557
SiwtingDown
DivSamp
14,899 0.413 0.579
0,478
0.586
BelFusion
9.172
0.420 0.507
0.512
0.530
BelFusion
9,026 0.413 0,585
0.468
0.587
HumanMAC
7.496
0.434
0,547
0.571
HumanMAC
0.381 0.530
0.568
CoMusion
8.747
0.409 0.497
0.527
0.523
CoMusion
7,253 0.378 0.546
0.472
0.578
Ours
5,400 0.402 0.489
0.534
0.541
Ours
4.993 0.395 0,581
0.504
0.589
TPK
6.412
0.388 0.473
0.452
0.472
TPK
6.522 0,422 0.529
0.509
0.560
DLow
11,603 0,358 0.433
0.439
0,452
DLow
11.549 0,400 0,515
0.490
0.537
GSPS
15.570 0.334 0.419
0.424
0.448
GSPS
14.822 0,466 0.485
0.472
0.530
Eating
BelFusion
DivSamp
15.681 0.321 0.419
5.954
0.310 0,381
0.428
0,418
0,420
0.445
Smoking
BelFusion
DivSamp
15.688 0.353 0.486
6.780 0.341 0.467
0,467
0.475
0.512
0.523
HumanMAC'
4.817 0,305 0.374
0.411
0.409
HumanMAC
5.415 0.339 0.475
0.445
0.501
CuMusion
6.149
0.295
0.395
CoMusion
6,802 0.311 0.443
0.427
0.458
Ours
3.461
0.303 0.371
0.420
0.413
Ours
4.222 0,322 0.447
0.451
0.486
TPK
6.779 0.555 0.615
0.571
0.598
TPK
6.631 0,480 0,584
0,526
0.568
DLow
11.897 0.530 0.590
0,561
0.564
DLow
11.680 0.441 0.541
0,497
0.534
GSPS
14,974 0,502 0.592
0.532
0.577
GSPS
15.000 0,400 0.514
0.475
0.529
Greeting
DivSamp
15.447
0.489 0.575
0.535
0.562
Waiting
DivSamp
15,455 0.387 0.517
0,486
0.535
BelFusion
8.482
0.482 0.544
0.524
0.540
BelFusion
7.747 0.390 0,507
0.471
0.511
HumanMAC
7.939
0.499
0,573
0.592
HumanMAC
6.506 0.385 0,532
0.496
0.557
CuMusion
8.946
0.481 0.556
0.558
0.552
CoMusion
7,690 0.358 0.484
0.487
0.476
Ours
5.444
0.473
0,551
0.554
0.551
Ours
4.434 0.368 0,506
0.515
0.538
TPK
6,410 0.377 0.475
0,468
0.507
TPK
7.384 0,560 0,694
0,592
0.665
DLow
11.542 0,343 0,444
0.451
0.487
DLow
11.882 0.490 0,566
0.539
0.570
GSPS
15,050 0.311 0.413
0.436
0.476
GSPS
13.746 0.459 0.564
0,530
0.587
Phoning
BelFusion
DivSamp
15,751
6.649
0.283 0.375
0.296 0.400
0.426
0.437
0.445
0.471
WalkDog
BelFusion
DivSamp
15.616 0.439 0.555
9.335 0,432 0.530
0.527
0.532
0.569
0.577
HumanMAC
5.069
0.287 0.383
0.405
0.431
HumanMAC
7.741 0.441 0.547
0.543
0.591
CoMusion
6.427
0.268 0.363
0.390
0.399
CoMusion
9.154 0.426 0,540
0.520
0.554
Ours
4.013 0,264 0.363
0.404
0.421
Ours
5,823 0.431 0,536
0,566
0.601
TPK
6.894 0.541 0.689
0.633
TPK
0.443 0.548
0.535
0.573
DLow
11.931 0.507 0.655
0,516
0.596
DLow
11.951 0.395 0.495
0,503
0.530
GSPS
14,310 0,485 0.663
0,502
0.606
GSPS
15.030 0,316 0,440
0.473
0.516
Photo
DivSamp
15,330
0.474 0.665
0.506
0.607
WalkTogether
DivSamp
16.095 0.321 0.458
0.486
0.525
BelFusion
8.446
0,434 0.601
0.462
0.546
BelFusion
6.378 0.296 0,393
0.484
0.495
HumanMAC
7.505
0.438 0,600
0,511
0.619
HunanMAC
4.336 0.298 0.387
0.447
0.454
CoMusion
8.923
0.422 0.606
0.503
0.611
CoMusian
6.512 0,270 0.372
0.435
0.431
Ours
5.522
0,445 0.591
0.531
0.590
Ours
4.128 0.256 0.357
0.449
0.451
TPK
6.520 0.466 0.538
0.542
0.565
TPK
6,708 0.455 0.533
0.538
0.558
DLow
11,875
0.442 0.521
0.510
0.525
DLow
11.904 0.428 0.518
0,516
0.539
GSPS
15.149
0.415 0.527
0.498
0.543
GSPS
14.797 0.351 0.469
0.490
0.528
Posing
DivSamp
15,429
0.395 0.499
0.510
0.541
Walking
DivSamp
15.964 0.373 0.535
0.508
0.547
BelFusion
8.438
0.406 0,510
0.498
0.531
BelFusion
5,116 0.367 0.471
0,530
0.546
HumanMAC
7.320
0.407 0.530
0.512
0.553
HumanMCC
4.306 0,321 0.447
0.472
0.485
CoMusion
8.236
0.393 0.501
0.492
0.499
CoMusian
6.487 0.308 0.443
0.447
0.465
Ours
5.143
0.389 0.497
0.532
0.537
Ours
3.522 0,263 0.416
0.452
0.474
TPK
7.450 0.505 0.522
0.535
0.538
DLow
11.947 0.430 0.422
0.493
0.477
GSPS
13,969
0.414 0.429
0.497
Purchases
DivSamp
14.967
0,388 0.404
0.502
0.478
BelFusion
10,272
0,410
0.409
0.494
0.472
HumanMAC
8.601
0.403
0.410
0,506
0.439
CuMusion
9.484
0.405
0.426
0.496
0.425
Ours
5.185 0.403 0.421
0.542
0.460
Table 6: Comparison of different methods on various classes and metrics.
17

---

model performance and inference efficiency. shows that reducing both noising steps during training and DDIM steps during inference 10 maintains competitive performance while drastically reducing computational costs compared to larger step configurations. This demonstrates our approach can maintain high prediction accuracy even with significantly accelerated sampling process, making it more practical for real-time applications.
local layer
total layer
APD
ADD
FDE
MMADEJ
MMFDEJ
8
5.013
0.372
0.484
0.529
0.542
5
10
4.766
0.361
0.467
0.515
0.533
12
4.458
0.347
0,452
0.513
0.535
12
12
4.229
0.354
0.461
0.520
0.536
4.587
0.362
0.458
0.522
0.540
Table 7: Performance comparison with different configurations of local fusion layers and total layers.
Noising steps
DDIM steps
APD1
ADE
FDE
MMADEL
MMFDEJ
1000
100
5.172
0,350
0.454
0.518
0.533
100
10
4.574
0.363
468
0.520
0.536
10
10
4.458
0.347
0.452
0.513
0.535
Table 8: Experiment results of the ablation study on diffusion steps
D Experiments on AMASS dataset
In Table 9
we report quantitative results AMASS dataset 33
As shown below, model still
achieves the best accuracy metrics (ADE&FDE), demonstrating strong generalization capability.
Model
APD1
ADE
FDE4
MMADE4
MMFDEL
Belfusion
9.376
0.513
0.560
0.569
0.585
MuManMAC
9.321
0.511
0.554
0.593
0.591
CoMusion
10.848
0.494
0.547
0.469
0.466
Ours
7.022
0.485
0.538
0.562
0.587
Table Performance comparison across different models on AMASS dataset
18

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The abstract and introduction have clearly included the motivations, important assumptions, and contributions made in the paper.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors? Answer: [Yes]
Justification: Thee authors have discussed the limitations of the work in the supplementary material.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (c.g.. independence assumptions, noiseless settings, model cell-speciiiiatinn asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low or images are taken in low lighting. Or speech-to-tex, system might not be used reliably to provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and faimesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
19

---

Answer:[Yes Yes]
Justification: The authors have clearly provided the full set of assumptions and complete proofs in the Method section.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors encouraged provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results the paper to the extent that affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
Justification: The authors have presented all the experimental details in the Implementation Details section.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is novel architecture, describing the architecture fully might suffice, or if the contribution a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, other means that appropriate to the research performed.
also be provided via detailed
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.
(b) the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is a new model (e.g., a large language model), then there should either way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reprodueibility may be tricky in some cases, in which authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers have some path reproducing or verifying the results.
5. Open access to data and code
20

---

Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Question:
Answer: [No]
Justification:
We will open-source the code after the paper is published.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips.co cc/
for more details.
While we encourage the release of code and data, we understand that this might not be possible, "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https guwdes/ddeeeee CooeeSumeeeeeeeeeeeeee ssi onyylicy) for more details.
/ //nips.co.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data,
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Question:
Answer: [Yes]
Justification:
The authors have presented all the training and test details in the Implementa- tion Details section.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [No]
Justification:Sinee the evaluation metrics are computed deterministically based on fixed procedures, without random components or repeated trials, we do not report error bars or statistical significance tests.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by emor bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
21

---

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out range (e.g. negative error rates).
If error bars reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification:
The authors have included sufficient information the computer resources in the Implementation Details.
Guidelines:
The answer NA means that the paper does include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental rums as well as estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't itdn't make it into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics httttp/://eeuiisp..ccpubl////// ctw.heeeeeiiccccceeeeeeeeeee
Answer: [Yes]
Justification: The authors conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics.
Guidelines:
The answer NA means that the authors have reviewed the NeurIPS Code of Ethics.
the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make to preserve anonymity (e.g., if there is special consid- eration due to laws or regulations their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: The authors have explained the broader impacts of the work at the end of the introduction and conclusion section.
Guidelines:
22

---

The answer NA means that there is no societal impact of the work performed.
If the authors answer NA No, they should explain why their work has societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deploymentt of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there negative societal impacts, the authors could also discuss possible mitigation strategies (c.g., gated release models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms monitor how system learns from feedback over time, improving the efficiency and accessibility ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: The paper poses no such risks.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification: The authors have cited the original paper that produced the code package or dataset.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (c.g., CC-BY 4.0) should be included for each asset.
23

---

For scraped data from a particular source (e.g., website). the copyright and terms of service of that souree should be provided.
assets are released, the license, copyright information, and terms use in the package should be provided. For popular datasets, paperssi thooe cww datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information is not available online, the authors are encouraged to reach out to the asset's 's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification: The new assets introduced in the paper are accompanied by detailed documen- tation, which is provided in the supplemental material.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/model part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset is used.
At submission time, remember anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question:
For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification:
The paper does not involve crowdsoureing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According to the NeurlPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsoureing nor research with human subjects.
24

---

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if' applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Does the paper describe the usage of LLMs if is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, originality of the research, declaration is not required.
Question:
Answer: [Yes]
Justification:
The paper describe the usage of LLMs.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.
Please refer to our LLM policy for what should or should not be described.
25


Reviews:
[{"review_id": "6eKUOoonoc", "reviewer": "Reviewer_n6UW", "summary": "This paper proposes WaveAR, which is a novel autoregressive (AR) framework for stochastic human motion prediction (SHMP). It applies the continuous autoregressive generation paradigm to the field of human motion prediction. It aims to solve the problem of detail loss and training instability caused by Vector Quantization (VQ) in the existing AR model when dealing with continuous input, and captures fine motion details by introducing wavelet transform.", "questions": "-  In the ablation study \"w/o DWT\" results indicate the effectiveness of the DWT module. Could the authors provide a more in-depth analysis, perhaps through visualizations or other quantitative metrics, to demonstrate how DWT specifically captures and leverages high-frequency details to improve motion prediction accuracy.\n\n-  The paper emphasizes WaveAR's computational efficiency, but specific inference time data (e.g., milliseconds per frame) or model size (number of parameters) compared to SOTA baselines are not provided.", "limitations": "The authors acknowledge some limitations in their supplementary material , specifically mentioning the trade-off between accuracy and diversity, which results in relatively lower diversity scores. Building on these points. \nAdditional limitations can be seen in Questions and Weakness sections.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strength**\n- The experimental results are truly impressive. WaveAR consistently achieves the most accurate predictions (lowest ADE and FDE) on standard HMP benchmarks like Human3.6M and HumanEva-I, significantly outperforming existing state-of-the-art methods. \n- WaveAR's flexible masking strategy extends its utility beyond just motion prediction; it excels in motion in-betweening tasks as well, showcasing its strong generalization capabilities across different application scenarios.\n\n**weakness**\n\n- In the introduction section, the author claims that WavAR is one of the \"first successful application\" as a contribution point, but the first one should not be an academic contribution, Instead, it should highlight the advantages of continuous autoregressive generation paradigm in this task. I think the author should re-summarize this innovation point.\n\n- The paper mentions that WaveAR maintains \"fast inference speed\" and \"computational efficiency\". However, beyond comparative results with existing methods in Table 1, specific inference time data are not explicitly provided. A detailed discussion on the model's performance and potential bottlenecks in practical real-time applications such as robotics control or VR would greatly enhance the paper's completeness.", "was_revised": true, "final_justification": "The authors have successfully addressed my concerns. After reviewing the other reviewers' comments, I believe this work features comprehensive experiments. However, the claim of novelty could be clearer. While there is an uncompared work, its unpublished status means it should not be a reason for rejection. I will maintain my \"Borderline Accept\" recommendation.\nI will keep ``Borderline  accept``.", "rebuttal": "To Reviewer n6UW\n===\nWe sincerely appreciate the valuable and insightful feedback from Reviewer n6UW. We will address all the concerns and polish our paper accordingly.\n\n**Weakness (1). advantages of continuous autoregressive generation paradigm**\n\nFirst, continuous generation avoids the inherent limitations of vector quantization (VQ) (e.g. the loss in mapping continuous motion sequences to discrete tokens[1], and the difficulty in optimizing the training process due to notorious codebook collapse[2]). Second, autoregressive generation has the following advantages: 1) The motion sequences are temporal, which is a natural fit with autoregressive modeling. 2) Better ability to capture long-distance dependencies and better ability to model long sequences. 3) Faster training and inference speed for autoregressive generation compared to diffusion's modeling of global distributions. \nThanks for your valuable suggestion about re-summarizing the innovation point. We will change this part in the revised version.\n\n**Weakness (2). inference speed computational efficiency**\n\nThank you for your suggestion. We evaluated inference time and parameter count on an NVIDIA V100 GPU by comparing three model variants of different sizes. Thanks to the reduced number of denoising steps, our model achieves faster inference while maintaining high prediction accuracy.\n| |Params|Inference time(s)|APD↑|ADE↓|FDE↓|\n|:----:|:----:|:----:|:----:|:----:|:----:|\n|HumanMAC|28.4M|1.25|6.301|0.369|0.480|\n|Ours(tiny)|26.8M|**0.21**|**4.952**|0.354|0.465|\n|Ours(small)|51.9M|0.43|4.458|0.347|0.452|\n|Ours(base)|86.5M|0.65|4.633|**0.342**|**0.450**|\nWe will add it in the revised version.\n\n**Questions (1). in-depth analysis of DWT**\n\nFirst, we visualize the spectrum of WaveAR w/o and w/ DWT, respectively. Since it is not possible to add images in the rebuttal, we count the percentage of high-frequency component averages. We randomly sampled 200 instances, and w/ DWT, the average proportion of the amplitude of high-frequency components is 0.2429, whereas w/o DWT, the average proportion of the amplitude of high-frequency components is 0.1624. This demonstrates that the DWT effectively captures the high-frequency component of the motion.\nSecond, we found some “static” phenomena in the generated motion, i.e., the person is hardly moving. After applying DWT, these cases will generate motion with more details instead of \"static\", , demonstrating that high-frequency information leads to richer motion details. Since it is not possible to add images in the rebuttal, we will update this part of the visualization to the supplementary materials in the revised version.\nWe also conduct an experiment on the ablation between DCT and Wavelet module. The results are shown below. We can see that incorporating spectral information improves motion prediction accuracy, and that DWT—by capturing high-frequency details in the input beyond what DCT can—yields even more precise predictions.\n| |APD|ADE|FDE|MMADE|MMFDE|\n|:----|:----|:----|:----|:----|:----|\n|w/o DWT&DCT|4.781|0.381|0.503|0.535|0.549|\n|w/ DCT|**5.016**|0.362|0.478|0.521|0.537|\n|w/ DWT|4.458|**0.347**|**0.451**|**0.513**|**0.535**|\n\n**Questions (2). inference time and model size**\n\nPlease refer to Weakness(2).\n\n\n**References:**\n\n[1] Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, and Li Cheng. Momask: Generative masked modeling of 3d human motions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1900–1910, 2024. 1, 2, 4\n\n[2] huanxia Zheng and Andrea Vedaldi. Online clustered codebook. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 22798–22807, 2023. 1"}, {"review_id": "ISWh3HuzXc", "reviewer": "Reviewer_fPmK", "summary": "This paper presents WaveAR, a method for stochastic human motion prediction by autoregressively making predictions in a continuous latent space. By doing so, the paper claims to be avoiding potential performance degradations due to discretized tokens. Furthermore, this neural motion representation is further extended by Discrete Wavelet Transform (DWT) features that are intended to capture multi-scale low- and high-frequency motion details. The model first uses a Spatio-Temporal VAE to compress raw 3D joint sequences into a continuous latent token stream, eliminating quantization errors. Next, a masked autoregressive diffusion process predicts future tokens, by leveraging DWT features. The proposed approach is evaluated on Human3.6M and HumanEva-I benchmarks, demonstrating a better or comparable performance.", "questions": "See my weaknesses section for the main questions. I have one extra question:\n\n1- (in line 3, abstract) “While autoregressive sequence models have excelled in related generation tasks, their reliance on vector-quantized tokenization limits motion fidelity and training stability.” Is there any evidence for this statement? The proposed ST-VAE can be similarly limited due to the inherent properties of the VAE formulation (e.g., reconstructing high-frequency details in the signal while ignoring the low-frequency ones).", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths:**\n\n1- Model Flexibility and Versatility: The model demonstrates significant flexibility. Beyond its primary function of forecasting, it can be effectively adapted for other tasks, such as motion in-betweening, by leveraging its flexible masking strategy.\n\n2- Competitive Performance: The model performs better than or on-par with the baselines on standard benchmarks. \n\n3- Integration of Techniques: A key strength lies in the novel adaptation of the MAR framework by guiding the autoregressive process with multi-scale features from a Discrete Wavelet Transform (DWT).\n\n**Weaknesses:**\n\n1- Limited Scope of Evaluation Benchmarks: The experimental evaluation is confined to the Human3.6M and HumanEva-I datasets. While these are standard benchmarks, the absence of tests on larger and more diverse datasets (e.g., CMU, AMASS) limits the assessment of the model's scalability and generalizability to more complex, in-the-wild motion.\n\n2- Omission of a Key Baseline Comparison: The paper acknowledges that its use of DWT features is inspired by MotionWavelet, yet MotionWavelet is not included as a baseline in the quantitative comparisons. Given that MotionWavelet also achieves strong performance, it is difficult to determine the precise advantage of WaveAR's more complex architecture (which includes the ST-VAE, fusion module, and diffusion head) over a potentially simpler, wavelet-focused approach. This omission makes the contribution of the additional components less clear.\n\n3- Potentially Overstated Novelty Claim: The central claim that WaveAR is \"the first successful application of a continuous autoregressive generation paradigm to [the] SHMP domain\" may be overstated. Earlier autoregressive models in human motion synthesis operated in continuous latent spaces before diffusion models became popular. For instance, HuMoR (Rempe et al., 2021) also predicts motion autoregressively in a continuous latent space. The paper's novelty claim should be refined to more accurately position the work within the existing literature, perhaps by emphasizing its combination of continuous autoregression with a wavelet-guided diffusion process.", "was_revised": true, "final_justification": "The main takeaway from this paper is the improved accuracy-diversity trade-off. In certain applications, more accurate predictions are preferable to more diverse results. However, I have concerns regarding the novelty and contribution claims. While the lack of a quantitative comparison against MotionWavelet is not grounds for rejection, as it is an unpublished work, it was the first to present a wavelet representation for human motion. Similarly, the other contribution claim is being the first paper to apply \"masked autoregressive modeling for human motion prediction.\" is not a novelty per se. Permutations of these techniques (e.g., discrete or continuous latent spaces, autoregressive modeling, masking, diffusion) have been used in previous motion modeling works (DartControl, MoMask, MLD), even if not specifically for motion prediction. Finally, the paper also requires a major revision regarding these contribution claims and the inclusion of additional AMASS results.\n\nThe reasons to reject—namely, the overstated novelty claims and the need for significant revisions—slightly outweigh the reasons to accept. Therefore, I will maintain my score of \"Borderline Reject.\"", "rebuttal": "To Reviewer fPmk\n==\n\nWe sincerely appreciate Reviewer fPmk's thoughtful comment and insightful feedback. Please see our detailed response below.\n\n**Weakness (1):Limited Scope of Evaluation Benchmarks**\n\nThank you for your suggestions. We conducted experiments on the AMASS dataset. As shown below, our model still achieves the best accuracy metrics (ADE&FDE), demonstrating strong generalization capability.\n| Model     | APD ↑   | ADE ↓  | FDE↓   | MMADE ↓| MMFDE↓ |\n|:--------- |:------ |:----- |:----- |:----- |:----- |\n| Belfusion | 9.376  | 0.513 | 0.560 | 0.569 | 0.585 |\n| HumanMAC  | 9.321  | 0.511 | 0.554 | 0.593 | 0.591 |\n| CoMusion  | **10.848** | 0.494 | 0.547 | **0.469** | **0.466** |\n| Ours      | 7.022  | **0.485** | **0.538** | 0.562 | 0.587 |\n\n**Weakness (2):Omission of a Key Baseline Comparison**\n\nThank you for your suggestion. We compare MotionWavelet with our model and get the following results. FDE measures accuracy on the final frame, while ADE measures accuracy over the entire predicted sequence. Although MotionWavelet achieves a better FDE, its relatively poor ADE suggests it can produce inaccurate predictions during intermediate frames. Since ADE is a more important metric for overall trajectory accuracy, our method clearly outperforms MotionWavelet. Moreover, because MotionWavelet’s code is not publicly available, we re-implemented its algorithm based on the paper and compared inference speeds, demonstrating that our model runs significantly faster than MotionWavelet. We will add the results to the final version.\n\n| Model|APD↑|ADE↓|FDE↓ |Inference time(s)|\n|:----|:----|:----|:----|:----|\n|Motionwavelet|**6.506**|0.376|**0.408**|  1.65|\n|WaveAR(Ours)     | 5.016| **0.362**|0.478 | **0.43**|\n\n**Weakness (3). Potentially Overstated Novelty Claim**\n\nWe appreciate the reviewer pointing out this issue. We agree that our original statement lacked precision and could lead to misunderstandings. To clarify, our approach is the first to apply masked autoregressive modeling for human motion prediction. We will correct our statement in the final version.\n\n**Questions (1). Evidence for the statement in line3**\n\nAutoregressive methods relying on VQ have achieved excellent performance on text-to-motion [1,2,3]. However, the presence of quantization error [2] and codebook collapse [4] makes it difficult to generalize these methods to other motion generation tasks. As noted in MARRS [5], VQ-VAE-based methods achieve poor performance for human action-reaction systhesis. For the human motion prediction, the results in Table 3 of our paper also demonstrate the instability of the VQ-VAE-based methods. For ease of reading, we have put the results from Table 3 in the paper into the rebuttal. When we replace our proposed ST-VAE with a VQ-VAE, the model’s performance degrades significantly across all metrics. \n|Loss|APD↑|ADE↓|FDE↓|MMADE↓|MMFDE↓|\n|:----|:----|:----|:----|:----|:----|\n|VQ-VAE|**7.233**|0.477|0.589|0.544|0.631|\n|WaveAR(Ours)|4.458|**0.347**|**0.451**|**0.513**|**0.535**|\n\n\nReferences:\n\n[1] Jianrong Zhang, Yangsong Zhang, Xiaodong Cun, Yong Zhang, Hongwei Zhao, Hongtao Lu, Xi Shen, and Ying Shan. Generating human motion from textual descriptions with discrete representations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14730–14740, 2023. 2, 3\n\n[2] Chuan Guo, Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, and Li Cheng. Momask: Generative masked modeling of 3d human motions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1900–1910, 2024. 1, 2, 4\n\n[3] kkasit Pinyoanuntapong, Pu Wang, Minwoo Lee, and Chen Chen. Mmm: Generative masked motion model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1546–1555, 2024. 1, 2, 4, 8\n\n[4] huanxia Zheng and Andrea Vedaldi. Online clustered codebook. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 22798–22807, 2023. 1\n\n[5] YB Wang, Shuo Wang, JN Zhang, JF Wu, QD He, CC Fu, CJ Wang, and Yong Liu. Marrs: Masked autoregressive unit-based reaction synthesis. arXiv preprint arXiv:2505.11334, 2025. 8"}, {"review_id": "3qhCmBdn03", "reviewer": "Reviewer_p7mw", "summary": "The paper presents WaveAR, a wavelet-aware autoregressive model for human motion prediction. The approach integrates frequency-domain features extracted via a discrete wavelet transform into masked latent representations using an attention-based fusion mechanism. A subsequent MLP module serves as a noise predictor within a diffusion process to estimate future motion sequences. The model is evaluated on the Human3.6M and HumanEva-I datasets and is compared against state-of-the-art methods using standard diversity and accuracy metrics.", "questions": "1. Could the authors provide a demonstration of the model’s robustness and generalizability on a recent, large-scale, and diverse dataset—such as AMASS, which has been adopted by several recent baselines?\n\n2. The authors are encouraged to further analyze the trade-off between accuracy and diversity, and to discuss whether their model still preserves meaningful diversity within each mode, rather than collapsing to a limited set of dominant motions. \n\n3. Could the authors provide more details or comparisons regarding the computational cost, inference speed, or model size of WaveAR relative to other baselines?\n\n4. Minor typos: L.145", "limitations": "The paper lacks a discussion of limitations. An analysis of issues such as the trade-off between accuracy and diversity, as well as potential failure cases, would contribute to a more balanced and transparent presentation of the work.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths: \n\n1. The paper includes thorough experiments including various ablations that analyze the contribution of each component offering insights into the model’s design choices.\n\n2. The integration of wavelet transforms allows the model to capture motion information at multiple temporal scales, which can help in representing fine-grained motion patterns.\n\n\n\nWeaknesses:\n\n1. While the authors evaluate on standard benchmarks such as Human3.6M and HumanEva-I, recent works (e.g., BeLFusion, HumanMAC, and CoMusion) have demonstrated results on AMASS—a larger and more diverse dataset that better captures a wide range of human motions. The absence of evaluation on AMASS limits the assessment of the model’s robustness and weakens the strength of its generalizability claims.\n\n2. While WaveAR emphasizes accurate motion prediction and achieves the best ADE and FDE scores, the improvement over previous methods is marginal and comes with a large drop in diversity, as indicated by significantly lower APD scores (e.g., compared to HumanMAC on HumanEva-I and CoMusion on Human3.6M). In the context of stochastic human motion prediction, diversity is critical to reflecting the multimodality of plausible future movements. For instance, in autonomous driving scenarios, a pedestrian turning right might vary subtly in trajectory or speed, and capturing these variations within each motion mode is crucial. This raises concerns about the model's ability to capture diverse and plausible future motions within each mode, which is essential for robust stochastic prediction. \n\n3. Although the authors mention that WaveAR is computationally efficient and fast, they do not provide discussion of computational cost, inference speed, or model size relative to other baselines.", "was_revised": true, "final_justification": "The rebuttal addressed some of my concerns regarding the benchmark evaluation and the trade-off between accuracy and diversity. I would like to raise my score to borderline accept.", "rebuttal": "To Reviewer p7mw\n===\nWe sincerely appreciate the valuable feedback from Reviewer p7mw. Our responses to each comment are provided below.\n\n**Q1: Results on AMASS dataset**\n\nThank you for your suggestions. We conducted experiments on the AMASS dataset. As shown below, our model still achieves the best accuracy metrics (ADE&FDE), demonstrating strong generalization capability.\n\n| Model     | APD↑    | ADE↓   | FDE↓   | MMADE↓ | MMFDE↓ |\n|:--------- |:------ |:----- |:----- |:----- |:----- |\n| Belfusion | 9.376  | 0.513 | 0.560 | 0.569 | 0.585 |\n| HumanMAC  | 9.321  | 0.511 | 0.554 | 0.593 | 0.591 |\n| CoMusion  | **10.848** | 0.494 | 0.547 | **0.469** | **0.466** |\n| WaveAR(Ours)      | 7.022  | **0.485** | **0.538** | 0.562 | 0.587 |\n\n**Q2：Trade-off between accuracy and diversity**\n\nThank you very much for your valuable suggestion regarding the analysis of accuracy-diversity trade-off. We acknowledge that our current model prioritizes prediction accuracy, resulting in relatively limited diversity. Previous methods have achieved higher diversity often at the expense of generating unrealistic or physically implausible motions  such as discontinuities in movement [1,2,3], an issue that does not arise in our proposed approach. To demonstrate the robustness and realism of our model predictions, we provide detailed results in the supplementary materials showing consistently accurate performance across various fine-grained action categories in the Human3.6M dataset. This further supports the validity and robustness of our method. Nevertheless, we recognize that the limited diversity remains a current limitation of our model. Improving diversity while preserving realism is an important future research direction. We will add this part in the Limitation section. In the future, we will pay more attention to this topic.\n\nReference:\n\n[1]Mao, W., Liu, M., Salzmann, M.: Generating smooth pose sequences for diverse human motion prediction. In: ICCV. pp. 13289–13298 (2021)\n\n [2]Dang, L., Nie, Y., Long, C., Zhang, Q., Li, G.: Diverse human motion prediction via gumbel-softmax sampling from an auxiliary space. In: MM. pp. 5162–5171 (2022)\n\n[3]Wei, D., Sun, H., Li, B., Lu, J., Li, W., Sun, X., Hu, S.: Human joint kinematics diffusion-refinement for stochastic motion prediction. In: AAAI. pp. 6110–6118 (2023)\n\n**Q3:Inference time and model size**\n\nThank you for your suggestion. We evaluated inference time and parameter count on  NVIDIA V100 GPU by comparing three model variants of different sizes. \n| |Params|Inference time(s)|APD↑|ADE↓|FDE↓|\n|:----|:----|:----|:----|:----|:----|\n|HumanMAC|28.4M|1.25|**6.301**|0.369|0.480|\n|Ours(tiny)|26.8M|0.21|4.952|0.354|0.465|\n|Ours(small)|51.9M|0.43|4.458|0.347|0.452|\n|Ours(base)|86.5M|0.65|4.633|**0.342**|**0.450**|\n\n**Q4:Lack of Limmitations:**\n\nThanks to the reviewer for the reminder. While WaveAR achieves strong average accuracy, it does exhibit two main limitations: Our masked autoregressive diffusion module focuses on minimizing reconstruction error over the average trajectory, which can lead to overly “safe” predictions, with diversity confined to a relatively small range of motion variations; Besides,current approach relies on standard joint‑coordinate datasets containing only about 17–32 keypoints, limiting its ability to capture finer movements .Enabling fine‑grained motion details is thus an exciting direction for future research.\n\n**Q5:Minor typos**\n\nThank you for the reminder of minor typos, we will correct them in the final version."}, {"review_id": "fFZz9nEMtP", "reviewer": "Reviewer_76BV", "summary": "This paper presents a new approach for stochastic human motion prediction. The proposed approach presents multiple components:\n- First, the motion is encoded as a sequence of continuous tokens using a spatio-temporal VAE (ST-VAE). In comparison  with the recent autoregressive approaches that rely on vector-quantized representations, this continuous latent representation allows for preserving more details.\n- Second, predictions are made in the latent space in an autoregressive fashion, conditioned on wavelet features extracted from the motion. This step combines a masked generative approach with a diffusion process. The wavelet transforms preserve more fine-grained features than the commonly used DCT, while the designed generation process allows for realistic, diverse predictions.\n\nExperiments on two widely used benchmarks demonstrate good performance compared to state-of-the-art approaches.", "questions": "In the rebuttal, I would like the authors to discuss the following points:\n- What are the limitations of the proposed approach, and what could be future improvements over WaveAR?\n- Ablations on the DCT vs. Wavelets\n- Discussion on the difference between training and inference settings (past always fully visible at inference, wavelet features computed only on the past at inference).", "limitations": "Limitations and potential negative impacts are not mentioned at all (the checklist says it is in the supplementary materials, but I could not find it).", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Strengths\nI feel like this paper has many strengths:\n- The approach is very well motivated: the introduction shows the current limitations of prior works and introduces the contributions that will address them.\n- The approach is novel: to my knowledge, this is the first that uses continuous autoregressive generation for human motion prediction.\n- The method is clear and well-explained. Despite the many components of the approach (tokenization, wavelet features, autoregressive generation with masking, diffusion process), the paper still reads nicely and is very understandable.\n- The masking allows the use of the same model for motion prediction and motion in-betweening.\n- The experiments follow the common practice, and the method obtains good results compared to the state of the art (better accuracy and very decent diversity).\n- Ablations are well-designed to demonstrate the interest of the technical contributions.\n- Visualisations given in the supplementary materials show good qualitative performance, with accurate yet diverse outputs.\n\n# Weaknesses\n\nI do not find major weaknesses in this paper. However, here are some points that I think could still be improved:\n\n- There is no discussion at all about the limitations of the method and future lines of research.\n- Additional ablation studies could be performed. In particular, it would be interesting to see how the model would perform with DCT features instead of wavelets.\n- The method section could be even clearer with improved notations. For instance, the symbol sigma is used for different purposes in Eqs. 1 and 10 (even if its meaning is clearly explained in both equations). L187, wouldn't it be clearer to use $L'$ instead of $T'$ as the sequence of $T$ poses is encoded to $L$ tokens before masking?\n- I think there is a small mistake L227-228: The ST-VAE is supposed to reduce the temporal dimension, but here, the length of the input and the tokenized representation is the same (H).\n- L145: The reference to the figure should be given (not fig. X).\n- From my understanding, at training time, the wavelet features contain information about the entire sequence, including the tokens that are masked. Wouldn't it be better to have something more similar to the inference settings at training time, with wavelet features computed only on the past, and masking done solely on the future?\n\n# Overall recommendation\n\nThis paper presents many strengths: the approach is well motivated and explained, novel, and obtains good results. Additionally, the ablations give insights into the most important parts of the model, and qualitative results are as impressive as quantitative ones. While I don't find major weaknesses in this paper, I feel that discussions on the current limitations and future works, as well as additional ablations, could improve it. I recommend \"Accept\".", "was_revised": true, "final_justification": "Following the rebuttal and discussions, I still believe this paper should be accepted. The approach presented in this paper is well-explained and novel: this is the first application of continuous autoregressive modeling for human motion generation, and the integration of discrete wavelet transforms in the framework is a valuable improvement. The model is flexible (it can be used for motion generation and in-betweening) and obtains strong performance on various benchmarks.\n\nIn the final version, I believe it would be important to add: (1) Ablation on the DCT vs. Wavelets; (2) Discussion of the limitations and future works; (3) Experiments on the AMASS dataset; (4) Information about running time and efficiency.\n\nDiscussion/comparison with MotionWavelet would be valuable even if it is not published work.", "rebuttal": "To Reviewer 76BV\n===\n\nWe sincerely appreciate Reviewer 76BV for the thoughtful review. Below, we provide detailed responses to each comment.\n\n**Q1：What are the limitations of the proposed approach, and what could be future improvements over WaveAR?**\n\nThanks to the reviewer for the reminder. While WaveAR achieves strong average accuracy (ADE&FDE), it does exhibit two main limitations: Our masked autoregressive diffusion module focuses on minimizing reconstruction error over the average trajectory, which can lead to overly “safe” predictions, with diversity confined to a relatively small range of motion variations; Besides,current approach relies on standard joint‑coordinate datasets containing only about 20 keypoints, limiting its ability to capture finer movements such as hand motions .Enabling fine‑grained motion details is thus an exciting direction for future research.\n\n**Q2:Ablations on the DCT vs. Wavelets**\n\nFrom the table, we see that incorporating spectral information improves motion prediction accuracy, and that DWT—by capturing high-frequency details in the input beyond what DCT can—yields even more precise predictions.\n\n| |APD↑|ADE↓|FDE↓|MMADE↓|MMFDE↓|\n|:----|:----|:----|:----|:----|:----|\n|w/o DWT&DCT|4.781|0.381|0.503|0.535|0.549|\n|w/ DCT|**5.016**|0.362|0.478|0.521|0.537|\n|w/ DWT|4.458|**0.347**|**0.451**|**0.513**|**0.535**|\n\n**Q3:Differences between training and inference process**\n\nThank you for your careful review and valuable suggestions. As stated in paper L167-L168 \"To inject these spectral cues into our autoregressive diffusion backbone, we apply vanilla DWT operation to the raw 3D–joint **history sequence** along both temporal and channel axes.\" The wavelet features are computed only on the observed history frames of length H at both training and inference time.The upper limit H+F in Equation (3) merely denotes the length of the input sequence for the wavelet transform computation. This may cause ambiguity and will revise it in the next version for clarity. For the masking strategy, the masking applied to the latent sequence during training is performed randomly across the entire sequence according to a predefined probability, not limited to future prediction frames. This flexible masking strategy enables our model not only to perform standard autoregressive prediction of future frames from history but also to support more complex tasks such as motion switching.\nIn summary, the wavelet features provide crucial spectral cues that enhance temporal understanding, and the autoregressive denoising diffusion model delivers strong generative power to ensure accurate and robust sequence reconstruction.\n\n**Q4:Minor typos**\n\nWe thank the reviewer for  careful attention in identifying these typos and ambiguities; we will correct them in the final version."}]

Author rebuttal:

Meta-review: This paper introduces WaveAR, an autoregressive framework for stochastic human motion prediction (SHMP) that operates in a continuous latent space without relying on vector quantization. WaveAR consists of two main stages: a lightweight Spatio-Temporal VAE (ST-VAE) compresses raw 3D-joint sequences into continuous latent tokens, and a masked autoregressive diffusion model, guided by multi-scale wavelet features, predicts future poses. The wavelet features, extracted via a 2D discrete wavelet transform, capture both low- and high-frequency motion details, enhancing prediction accuracy. A fusion module combines these spectral cues with temporal context using alternating cross-attention and self-attention layers. Experiments on Human3.6M and HumanEva-I benchmarks demonstrate that WaveAR achieves state-of-the-art performance in accuracy and efficiency.

The integration of continuous autoregressive modeling with wavelet transforms is a clear advancement over prior discrete or DCT-based approaches. Ablations validate the contributions of DWT and ST-VAE, showing improved high-frequency detail capture. WaveAR shows superior accuracy metrics and faster inference compared to baselines. However, the reviewers have raised several concerns. The model prioritizes accuracy over diversity, which may limit its use in some scenarios. Initial claims about being "first" in continuous autoregressive modeling were overstated, as similar techniques exist in motion synthesis. Also, it lacks a quantitative comparison against MotionWavelet.

During the rebuttal phase, the authors have made great efforts in addressing the concerns. For the concern of missing large-scale dataset validation raised by Reviewers p7mw and fPmK, the authors added AMASS results showing superior accuracy. For the concern of DWT vs. DCT ablations raised by Reviewer 76BV, the authors showed DWT’s superiority in capturing high-frequency details. For the concern of inference speed raised by Reviewer n6UW, the author provided inference times and model sizes. Most reviewers are satisfied with the rebuttal. After the discussion, all the reviewers agree that the comparison with MotionWavelet should not be grounds for rejection, as it is an unpublished work.

The paper’s technical merits, coupled with thorough rebuttal responses, outweigh its limitations. The authors are encouraged to improve the paper by considering the reviewers’ comments carefully.
Decision: Accept (poster)