FlashMo: Geometric Interpolants and Frequency-Aware Sparsity for Scalable Efficient Motion Generation
Authors: Zeyu Zhang
Yiran Wang
Danning Li
Dong Gong
Ian Reid
Richard Hartley
Venue: NeurIPS 2025 poster ()
Keywords: Motion Generation
Diffusion Model
Primary area: applications
TL;DR: FlashMo introduces a geometric factorized interpolant and frequency-sparse attention, enabling scalable efficient 3D motion diffusion.

Abstract: Diffusion models have recently advanced 3D human motion generation by producing smoother and more realistic sequences from natural language. However, existing approaches face two major challenges: high computational cost during training and inference, and limited scalability due to reliance on U-Net inductive bias. To address these challenges, we propose **FlashMo**, a frequency-aware sparse motion diffusion model that prunes low-frequency tokens to enhance efficiency without custom kernel design. We further introduce *MotionSiT*, a scalable diffusion transformer based on a joint-temporal factorized interpolant with Lie group geodesics over $\mathrm{SO}(3)$ manifolds, enabling principled generation of joint rotations. Extensive experiments on the large-scale MotionHub V2 dataset and standard benchmarks including HumanML3D and KIT-ML demonstrate that our method significantly outperforms previous approaches in motion quality, efficiency, and scalability. Compared to the state-of-the-art 1-step distillation baseline, FlashMo reduces **12.9%** inference time and FID by **34.1%**. Project website: https://steve-zeyu-zhang.github.io/FlashMo.

FlashMo: Geometric Interpolants and Frequency-Aware
Sparsity for Scalable Efficient Motion Generation
Zeyu Zhang
Yiran Wang
Danning Li Dong Gong lan Reid
Richard Hartley
ANU MBBZAA UNSW
https://stave-eyy---nngggggg io/Fassho
Hi-Fi
A person is doing karate moves.
A person is dancing in the spot.
Lo-Fi
Figure 1: Frequency of human motion. The diagram illustrates that dynamic motions exhibit higher frequencies, while static motions correspond to lower frequencies. This observation provides key insights for the frequency-aware sparsification design of our FlashMo.
Abstract
Diffusion models have recently advanced 3D human motion generation by pro- ducing smoother and more realistic sequences from natural language. However, existing approaches face two major challenges: high computational cost during training and inference, and limited scalability due to reliance U-Net inductive bias. To address these challenges, we propose FlashMo, frequency-awer awaree sparse motion diffusion model that prunes low-frequency tokens to enhance efficiency without custom kernel design. We further introduce MotionSiT, a scalable diffu- sion transformer based on joint-temporal factorized interpolant with Lie group geodesies over SO(3) manifolds, enabling principled generation joint rotations. Extensive experiments on the large-scale MotionHub V2 dataset and standard benchmarks including HumanML3D and KIT-ML demonstrate that our method significantly outperforms previous approaches in motion quality, efficiency, and scalability. Compared to the state-of-the-an I-step distillation baseline, FlashMo reduces 12.9% % inference time and FID by 34.1 %.
Introduction
The conditional generation of 3D motions has recently garnered significant attention due to its broad applicability across various domains, including robot manipulation [65], urban planning [11], virtual
"Equal contribution.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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[83] and augmented reality [87], game development [21, 101], and video creation [5]. Notably, recent progress text-to-motion generation, particularly with autoregressive [100, 60, 59, 30, 85, 39, 92] and diffusion models [70, 93, 7, 94, 44, 73], has enabled the synthesis of natural human motion from natural language. While VQ- VAE--badd autoregressive methods achieve outstanding quantitative results, they generate less natural motion with jitters due to frame- wise noise arising from directly decoding discrete tokens, and fine-grained motion details are sometimes lost during token discretization [13]. In contrast, motion diffusion models generate smoother and more realistic human motion, showing a promising trend in human motion generation [73, 74, 95], However, despite their strengths, diffusion-based approaches still face two significant challenges, collectively limiting their applicability in real-world scenarios.
(1) Efficiency. Motion diffusion suffers from high computational cost, as well as long training and inference times [41]. Existing efficient methods such as step distillation [17, 16, 41], step reduction [104], and linear models [99, 89] either require additional training of teacher models or involve complex scanning mechanisms, resulting overengineering and low efficiency.
(2) Scalability. Recent works in image [24, 54, 48, 71] and video generation [82, 72] indicate that the U-Net [64] inductive bias is not critical for diffusion [24], while plain transformer denoisers show promising scalability. However, existing motion diffusion models are mostly restricted conventional U-Net architectures on smaller datasets [31, 61], resulting in limited and unexplored scalability.
To tackle the first challenge without additional engineering, we leverage intrinsic data characteristics. We observe that dynamic motion, which is more important [97, 100], exhibits higher frequency compared to static motion in generation process, as shown in Figure 1. Hence, we design a frequency- aware sparsification mechanism that dynamically prunes tokens corresponding motion, enhancing efficiency while preserving high frequency motion at the attention head level. This structured sparsification allows seamless adaptation to hardware-efficient exact attention [19, 18, 67] without the need to customize kernels, which further enhances the efficiency. Furthermore, the unified training and inference sparsification strategy resolves the sparsity granularity gap, enabling 2.25x speedup over full attention while maintaining comparable performance.
low frequency
To tackle the second challenge, design MotionSiT, along with FlashMo, tailored for motion diffusion, which differs from the standard SiT that interpolates all dimensions together in Euclidean space. Instead, perform temporal-spatial factorized interpolant with Lie group geodesies on the manifolds of joint rotations. This benefits our diffusion on the so(3) representation of joint rotations [26, 37] and makes it more feasible scale training on larger datasets [50]. Comprehensive analyses also validate the effectiveness of each component and their synergistic integration.
Furthermore, to demonstrate the efficiency and scalability of FlashMo, we pretrain on the large- scale open-source dataset MotionHub V2 [50] and evaluate our method on standard cext-to-motin benchmarks, including HumamML3D [31] and KIT-ML [61]. The results in Figure 5 show our method significantly outperforms prior approaches in terms of motion quality, efficiency, and scalability.
The contributions of our paper can be summarized follows:
We present FlashMo, a frequency-aware sparse motion diffusion that prunes low-frequency tokens to improve efficiency without kernel customization. Moreover, our trainable sparsification strategy eliminates the sparsity granularity gap between training and inference, achieving a 2.25x speedup over full attention without sacrificing performance.
We introduce MotionSiT, diffusion transformer with factorized Lie group interpolant that enables scalable motion diffusion on S3)) manifolds of joint rotations.
To evaluate our method's efficiency and scalability, we conduct comprehensive experiments on the pretraining dataset MotionHub V2 [50] and downstream benchmarks including HLmanML3D [31] and KIT-ML [61]. The results show that our model outperforms previous methods and achieves outstanding balance between efficiency and performance. Compared to the state-of-the- art 1 I-step distillation baseline (MotionPCM)) FlashMo reduces inference time by 12.9% and FID by 34.1%.
2 Related Work
Motion diffusion. Diffusion models have been widely adopted for human motion generation due to their strong ability to synthesize realistic and diverse sequences [70, 93, 7, 94, 44, 73, 95, 42, 80,

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(a) MotionSi
Sparse Transformer
(0) Frequescy aware Spansification
Noise
Motion
Latent
Linear and Reshape
FFN
Guide Token Pruning
Layer Norm
Artention Spores
Layer Norm
Hi- Heads
Lo-Fi Hends
Sparse Transformer
Sparse Transformer
Sparsc-MHA
QKY
KV
Embed
Layer Norm
X
CLIP
Noised
Latent
Figure 2: FlashMo architecture. FlashMo leverages frequency- aware sparsification and MotionSiT backbone to efficiently and scalably generate motion from noised latent inputs.
12, 38, 8, 74, 43, 90, 76, 33]. However, the time-consuming problem has motivated recent efforts of acceleration. Existing approaches include step distillation [17, 16, 41], GAN-based discriminators [104], and lightweight linear models [99, 98, 97, 89]. However, these methods often either rely on multi-stage training with teacher models, require additional discriminators, or trade-off performance for efficiency. Moreover, recent motion diffusion methods have explored motion frequency for designing consistency losses [6] or decomposing hidden states linear models [47]. However, they have not investigated its role at the attention hhad level or its potential for further improving efficiency
[57]. These methods have also been applied downstream tasks such as long motion generation
[66, 2] and motion editing [79, 62, 92, 39, 95, 14].
Sparse attention. Sparse attention manifests in two forms: token sparsification and activation sparsification. Depending on the granularity of sparsity, token sparsification can be categorized into unstructured, semiistruuttured, and structured schemes. Unstructured sparse attention [75, 55] applies token sparsification without fixed attention pattern, resulting in hardware unfriendly and challenges in kernel design [22]. Semi-structured sparse attention performs token sparsification with: (1) pyramid [102], local [86, 3, 78, 88, 105, 51], or sliding windows [96, 34, 53, 84], (2) predefined attention mask [40, 77, 56], (3) introducing N M sparsity into attention weights [25, 23, 10], according to the observed attention patterns, (4) block-wise sparse attention with pooling operation [81, 45, 27, 84]. However, often requires custom computational kernels tailored each sparsity pattern or dedicated hardware support. Structured sparse attention [4, 63, 1, 52, 103, 35] prunes tokens before the attention computation, enabling acceleration without the need for custom kernels. However, due to its coarse granularity. it sometimes underperforms and is therefore typically accompanied by distillation [49, 46), which introduces additional computation to pretrain the full attention teacher model. For activation sparsification, it reduce computational cost by zeroing insignificant activations [91, 68, 29].
3 Method
3.1
Overview
Our FlashMo introduces innovations in both the formulation and architecture of motion diffusion, as shown in Figure 2. We propose a geometric factorized interpolant that respects the temporal-spatial structure of motion and preserves joint rotation consistency via manifold based interpolation. This allows us to achieve superior performance and improved scalability. To improve efficiency, we design sparse attention layer that prunes lowfreequncy tokens while preserving high- frequency ones at the head level. Token selection is adaptively guided by the attention distribution, enabling unified training and inference without the sparsity granularity mismatch. Our sparsification integrates seamlessly with harfware-efficient exact attention [19, 18, 67] without kernel customization, achieving high efficiency without sacrificing performance.
frequency-
3

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3.2 MotionSiT
MotionSiT is latent generative model. Given motion representation M M where L denotes the motion length and D the spatial feature dimension encoded from rotations, we apply a VAE to compress M along the temporal axis. The encoder maps M to a latent representation where T = /./r is the reduced temporal resolution with compression ratio r; and S is the latent dimension in spatial direction. Compared to standard SiT as in Appendix /, which targets image generation, motion generation differs due to its temporal-spatial structure and the manifold geometry of motion representations. According to these characteristics, our MotionSiT introduces following geometric interpolant tailored for motion.
E RT*
Temporal-spatial factorized interpolant. The original SiT assumes each input sample is flat vector and defines a simple linear stochastic interpolant between noise sample ~ ~V, 7) and the data sample x² via:
2y a(t)²* + tt))e.
While effective for image data, this formulation overlooks the heterogeneous nature of motion representation, where temporal and spatial dimensions carry distinct physical meanings and roles in motion representation.
Hence, introduce temporal-spaticl factorized inter- polant (FT), which applies independent noise schedules along the temporal and spatial dimensions. Specifically, we define:
Geodesic Int er poleent
F1 = aT( ats(t) Ox x² at)e,
Interpolant
where (t) RR and (t( E R are scalar schedules for temporal and spatial dimensions, respectively. The element-wise product is broadcasted to modulate x² at each timestep and spatial location individually. This factorization allows flexible noise control across time and joints, reflecting motion's structural characteristics.
Motior
Nanifold
Lie group geodesics interpolant. Another limitation of interpolation in vanilla SiT lies in its assumption of Fuclidean geometry, shown in Figure 3 However, for motion representation. joint orientations are commonly expressed as rotation matrices in SO(3) [26, 37]. Applying such interpolation directly the motion manifold leads to geometric inconsistency in joint rotations.
Figure 3: Euclidean interpolant vs. Lie group geodesics interpolant.
Hence, introduce the Lie group geodesic interpolant (LI) respect the underlying manifold structure through Lie group geodesics. Given a target motion sample x* E 9, where 9 denotes a Lie group, we first map x* its tangent space g using the logarithmic map log 9 g. We then interpolate within the tangent space before mapping back to the manifold with the exponential map:
Ft Exp (o(t) log(x")) + aat)
where is Gaussian noise in the Lie algebra g. This approach ensures that the interpolated sample sr always lies on the manifold and that noise is injected in way that respects the local geometry of the motion space.
Geometric factorized interpolant. Building on the temporal-spatial factorization introduced earlier, now incorporate the Lie group geodesic define the geometric factorized interpolant (GFI) in MotionSiT. Specifically, for motion representation lies on manifold such as 8O(3), we perform geodesic interpolant on both temporal and spatial dimension:
Exp (oT()), ats())s log(<"" TTT))
where *Te denotes the motion state at time and spatial location 8, and et is Gaussian noise in the corresponding tangent space. This formulation combines the benefits of temporal-spatial control while preserves the geometric consistency of joint rotations during interpolation.

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Hi-Fi
Lo-Fi
Frequency
Ileatmap
Figure 4: Frequency magnitude vs. attention heatmap of attention heads. The frequency magnitude is computed with the Fast Fourier Transform (FFT) and averaged over 100 latent motion features. Brighter colors indicate higher magnitudes. Pixels closer to the center represent lower frequencies. Both maps are visualized from the last layer of MotionSiT.
3.3 Frequency-Aware Sparsification
Through observation of the frequency of attention heads in the upper part of Figure 4, the heads can be categorized into Hi-Fi and Lo-Fi groups. Moreover, the lower part of Figure 4 shows that Hi-Fi and Lo-Fi heads exhibit clearly distinet patterns in the attention heatmap, providing strong empirical evidence for designing dynamic token sparsification.
Frequency-aware head partition. Given the number of head N₁. we first partition the N₂. attention heads based on their frequency with an ratio E [0, 1]. where the first 3N₁, heads are low-frequency (Lo-Fi) heads H.o and the remaining N), heads are nigh-freueeccy (Hi-Fi) heads HHi-
HIo C {l,...,Nnh, with |Hol BNN, and Hin {1, ..... N,. Hio
Full attention for Hi-Fi heads. Given motion latent representation X E RTx55, for each attention head E HHi we define projection matrices (h(h) w((h)) w! Sxdx, where de is the hidden dimensions for a head. We project X into Q, K. V for each head:
Q (h) (W()), 9, K[X] (h) XW V[X = ( E
where Q (h) K (b),
V[X (M) Txdx, We then calculate full attention for each Hi-Fi head:
Attention (Q[x) K (h) V[X
where h E Hmn
Sparse attention for Lo-Fi heads. We perform attention-guided sparsification on Lo-Fi heads, adaptively pruning less important low-frequency motion tokens while preserving key motions
Attention-baue based adaptive token selection. Previous works [100] have successfully incorporated eecettionguuiance for selecting key motion tokens. However, they are limited attention masking and do not adapt the masking ratio across layers. In contrast, our sparsification is adaptive across layers based on their attention distribution.
Given full attention score in each head A Q[X ( E T'xT, the cumulative attention score 0g for each token is calculated by summing the corresponding column, and the normalized cumulative attention score az by averaging over the non-zero entries in that column.
E
A ll[A,
0g Σ A
T Σ
+ 0
We then select the minimum number of tokens whose cumulative attention scores reach the threshold of the total attention mass:
k min
@sorted(s) 2
(so)ted())
5

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where (corted(1) denotes the i-th highest attention score, obtained by sorting cumulative attention scores a in descending order.
We then select the top k tokens from E R according to their normalized cumulative attention scores azg and obtain the selected token matrix Z RR*SS where B T. This allows vary depending on the distribution of attention scores in each attention layer:
Tx Tāp= Z :]
Z E R
Sparse atlention calculation. For each attention head h E Hi.. we project X into Q, and Z into K, V for each head:
Q (
k = Zwk (N) XW q
V(zj(M) ZW (h)
&
where Q Q()](A) each Lo-Fi head:
R xdp, and K
I
(h) V Z (h) E Rexd, We then calculate sparse attention for
Attention AAtentionQ([X]" (h)
K K (M)
viz where E HLo-
Sparse multi-head attention. calculated as:
Hence our frequency-aware sparse multi-head attention can be
{
Attention (Q Q
(h) K X (N), V(X
h E Hmi
Sparse-MHA(X) = Concat
W.
Attention((]] (h) K [Z] (h), ((
h E Hio
where W. dx) projects back the original latent dimension.
Experiments
4.1 Datasets and Evaluation Metrics
For pretraining, we utilize the recent large-scale open-source dataset MotionHub V2 [50], which contains 142,350 motion clips and 259,998 captions. For downstream evaluation, we conduct experiments standard rext-to-motion (T2M) datasets, including HumanML3D [31] and KIT-ML [61]. HumanML3D comprises 14,616 motion clips, each accompanied by three textual descriptions, resulting in total of 44,970 captions. The KIT-ML dataset consists of 3,911 motions paired with 6,278 textual descriptions. For both datasets, we adopt the pose representation defined in T2M [31] to ensure consistency in motion representation across evaluations.
Datasets.
Evaluation metries. We adopt standard evaluation metrics to assess different aspects of our experiments. We use FID and R-Precision to evaluate the realism and accuracy of generated motions, MultiModal Distance to measure motion-text alignment, and a diversity metric to quantify variation in motion features. Additionally, we employ the Multi-Modality (MModality) metric to assess diversity among motions generated from the same text description. Moreover, calculate Average Inference Time (AIT) for showing efficiency.
4.2 Implementation Details
The encoder and decoder of the VAE consist of 4 layers with compression rate 4. MotionSiT
has depth of 8, with frequency ratio 3 = 0.5 and an attention threshold γ 0.95. Both the VAE and MotionSiT use attention heads with a latent dimension of 512. We employ a frozen text encoder from CLIP ViT-B/32. A constant learning rate of x 10 is used, with a batch size of 256 and the AdamW optimizer. For fair comparison, each model trained for 6K epochs during the VAE stage and 3K epochs during the diffusion stage. We adopt 1000 diffusion steps during training and 10 sampling steps during inference. All experiments are conducted on an Intel Xeon Platinum 8469C CPU at 2.60 2.60GHz, with single NVIDIA H20 96G GPU and 32GB of RAM.

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Table 1: Comparison of text-to-motion generation on HumanML3D [31] and KIT-ML [61] datasets. + indicates the closer to real data, the better. Bold and underline indicate best and second best results.
Method
Venue
ATT
R- R-Precision
FID
MM Dist
Diversity
MModalitt
Top-1
Top-2
1op-3
HLmanMLaD [3]]
Real
(1G11 (00
0.703= 033
0.797 oe
0.002 000
2,974 me
9.50g 945
MMM (60)
CVP8 2024
0.081
(15044
0.00G
0.794 oe
0,080=
2.308 107
9.41
1.164
McMask [30]
CVPR 2024
0.121
(L.21
0.713=002
0.045=007
2.958+
1.241
BAMM [59]
BCCV 2024
0.411
0.525
0,720 033
0.814
0.055 -012
2.919 100
9.717
1
MoGenTTS [85]
NeurIPS 2024
0.529
0.719=492
0.812*.00
0.003=/00
2.867 10G
9.5704.007
MctionLCM [17] (1-sic)
BCCV 2024
0.030
0.502
0.701
0.803
0.487
3.022 000
631 9.006
2. 2.172
MationL.M [17] (2-ipp)
ECCV 2004
0.035
0.505
0,705-992
0.505=-00
0.368
2 2,886*-Me
2. 1.187
Mationl CM [17] (4-s0p)
RCCV 2014
0.043
0.502
0
0.304=-M12
3.012 MTT
2 2:250
EMDM [104
BCCV 2024
0.050
0.3984
0.684 036
0.780 600
0:112 019
3.110 em
1.641 mns
Motion Mamba [99]
BCCV 2024
0.058
(1.522
0.003=002
0.7924+006 me
0:281 0:281=99
3.000 (C4
9.871 9-564
2.254
StaklaMooFusion [35]
MM 2024
0.499
0.513
0.748
01841
0.008=-911
9.748
1.774
MotionL.CM--V2 [16] (1-stop)
Preprint 2014
0.031
0.743 003
0.837 ton
0.072= 9333
2.707 107
9
1.858 (EA
Motionl CM-V2 [16] (2-step)
Preprint 2014
0.038
0.551
0.745
0
0.0:0 0033
2.765 me
0.5842.206
1 1.833 (82
MotiunLCM-V2 V2 [16] (4-step)
Preprini 2024
0.050
0.5553
0,766-993
0.8377.000
0.056-9911
2.773
9.596
1. L.758
Lighi-T2M [89]
AAAI 2025
0.151
0.511
0.600-902
0,795*0000
0.040=-912
3,002 U0e
1 1,677=
MationPCM [41] (1-step)
Preprint 2025
0:031
0.590 u2
0.702-.011
0.84
0.04g==011
2.711 10
9.530
1.772 ONT
MstionPCM [4]] (2-step)
Preprini 2025
0.038
0.555
0.709=000
0.839=
0.033
2.739 WT
9.618 66188.000
1.76y*
MotionPUM [4]] (4-step)
Preprim 2025
0.045
0,702=913 933
0.822
2.716
9.575
1.714
FlashMo (Ours)
0.027
0,01
900
2.711 2.100
9.614
2.812
FlashMo w/ peetrain (Ours)
0.027
0.568 000
0.761 A02
0351
1.000
0.029 0022
2,703
100
9.801 9.20%
KIT-MI [ol]
Real
014224
0.649
0.775
0.011 0
2.788*
11.08
MMM 160)
CVPR 2024
(1.014 D0
0,621=003
0.744
0.316 023
2.977 0lo
10.91 8-304
1,252 (GD
MoMask [30]
CVPR 2024
0,025 oor
0.650
0.782
0.204-011 011
2.7794
1.1314.04
BAMM [59]
BCCV 2024
0.788c-006
0:183=
2.725*
(99
11.01 9.004
1.60y
MoGGeTS [85]
NeurIPS 2014
0.445
0.671
0.707-1000
0.143 001
2.711
10.90
EMUM
BCCV 2024
0,000 A04
0.75g+000 00G
0.261 011
2.874 01G
10.90 A- 9-366
1,345 (60
Motion Mamba [99]
BCCV 2024
0,019
0.645
0.765
0.307
3.021
11.02 1.008
1.678
StuhieMoFusson [38]
MM 2024
0.465
0.660-901
0.782*.004
0,258 0
10.94
1. 1.582
Light-T2M [89]
AAAAI 2025
0.444
0.670-2937
0.794
0.161=
2.746 mn
1 1.005
MotionPCM [41] (1-step)
Preprint 2015
0.433
0 907
0.78) coe
2.820 002
10.78- 57%
1.3372 64T
MotionPCM [41] (2-step)
Preprimt
0.3377 000
0.064 035
0.787 mo
0:201 011
2.841 018
10,83 864
1.254 (CD
MotionPMM [4]] (4-step)
Preprint 2025
(1.053
0.064
0.3316
2,881 66a
10,70
1.258
FlashMo (Ours)
0.480
0.670=-034
0.709
0.152
2 2,700
10.64
3 3.287
FlashMo w/ pretrain (Oun)
0.453 101
0.679 0011
0.807 400
0.132 (w15
2.701
70.79 M08
3.591 2 470
250
309
110-
150
2-
WD
Prabbito 199)
(a) FID vs. AIT(s) /
(b) Training Time 4
(c) Parameters 4
(d) GFLOPS 4
Figure 5: Efficiency comparison. The figure demonstrates that FlashMo achieves the lowest inference time, training time, model size, and FLOPs while maintaining superior performance compared to other methods.
4.3 Comparative Study
We compare our method with recent efficient motion diffusion models and VQ- NQ-VAE-basee models on both HumanML3D and KIT-ML. We include both our model trained from scratch and the version pretrained on MotionHub V2 [50]. And we follow T2M [31] and report the average over 20 runs with 95% confidence intervals. The results in Table demonstrate that our method consistently outperforms other approaches across most quantitative performance metrics, achieving a 10% effi- ciency improvement compared previous fastest 1-step distillation [17], without the need to train an additional teacher model. Please see the full comparison table in Appendix D.
Efficiency. We compare our method with other approaches in terms of Average Inference Time (AIT), training time, model parameters, and GFLOPs. For the inference setting, we follow [7] to calculate AIT, measured on the same Tesla V100 GPU. The results in Figure 5 and Table 1 demonstrate that our method not only achieves superior performance but also maintains the lowest inference time, training time, model size, and GFLOPs.
7

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0.57
0.364
0566
0.76
0:257
0.250
0:761
0.805
0.850
0.551
0.25
0.56
0.850
RiPrcction T0p3
Nchecciion Top-2
0249
0762
Rohwessoon Top-3
0.74
0.235
0.845
0.640
8.6
535
535
0.535
0.53
0.333
0,02
0.72
730
0.712
0.71
0.835
.35
0.833
0,832
0.854
0.84
0.51
0.71
0.830
25%
50%
50%
25%
50%
100%
Pretrain Data Proportion
Pretrein Date Proportion
Pretrain Data Proportion
(a) R-Precision Top-1
(b) R R-Prrcissinn Top-2
(c) R- R-Prrcision Top-3 f
107
0-310
2.77
2,700
2.0
0.10
0.08
2.75
745
2 12
2.700
MWoocalty
2.74
0.022
0.009
2.73
2.5
643
0.04
2.03
2.71
2.708
2.705
2.70
0%
25%
50%
75%
100%
0%
25%
50%
75%
100%
0%
25%
50%
75%
100%
Protroin Data Proportion
Protrain Data Proportion
Protrain Data Proportion
(d) FID
(e) MM Dist 1
(1) MModality f
Figure 6: Scaling trend. The figure demonstrates the scaling trends of different denoiser designs (U-Net, DiT, SiT, and MotionSiT) with varying proportions of pretraining data. The results show that our MotionSiT exhibits superior scalability and outperforms other methods.
Table Interpolants design. The model is trained from scratch on HumanML3D [31]. The right arrow + means that the closer to the real motion, the better. Bold indicates the best result.
R Precision
f
Method
FID4
MM Dist
Diversity
MModality
Top 1
Top 2
Top 3
Real
0.511+0008
0.703 003
0.797 002
0.002
2.97/4
9.50
SBDM-VP
0.00
0.705 005
0.791 4: 003
0.094
2.015
2.774
009
9.6-12#.078
2. 2.209 1. 2. 063
Linear
0.535+.001
0.733+t0004
0.837 00G
0.065 4.09
2.744
007
9.595 009
2. t.4
GVP
0.5.12t.005
0.741 +t.00000 0033
0.840=#.00 001
0.058 .022
2.736=t.006 008
9.588 -443
2.600 009
FI (Linear)
0.511/1=001
0.743=
0.842 000
0.05
2.732
9.590+.0777
2.659ttoom
HI (GVP)
0.507
0.710 0002
0.802 D0G
0.089
2.784+
9. 0.6322.057
2.218
LI (Linear)
0.551 t5.05
0.748
0.844
000
0.048 1448+.002
2.730
9. 0.6022.007
2. 2.705
LI (GVP)
0.550
0.745- 001
0.841 = 003
0. 0.0444.005
2.725:1.012
9.633
2. 2.788-1.77
GFI (Linear)
0.562 1.004
0.754 000
0.847 0005
0.041 1.0002
2.711 2.006
9.614 +5500
2,812 1.006
4.4
Ablation Study
Scalability. To showcase method's scalability, we compare various denoiser architectures against our backbone using different proportions of pretraining data. As shown in Figure 6, our method consistently improves performance as data increases, demonstrating strong scalability and generalization capability. This scaling trend shows promising results for 3D human motion generation, larger amounts of motion data can be acquired by HMR [28, 15] and MoCap [20, 36), highlighting a promising direction for foundational motion generative models.
Interpolants design. The interpolant approach is one of the key innovations in FlashMo. Since we propose the geometrically factorized interpolant, the choice of an appropriate interpolant function becomes particularly important. Both score-based [69] and velocity-fiee -ield diffusion models [54] have explored different interpolant functions att) and variances (tt), as discussed in Appendix A. The results in Table 2 show that our geometric factorized interpolant with linear interpolation function significantly outperforms other interpolants. Furthermore, when leveraging the factorized interpolant, the linear function exhibits notable advantage compared to GVP, which contrasts with SiT [54] where GVP demonstrates better performance. This is mathematically sound because although factorization better aligns with the temporal-spatte structure of motion, combining it with
8

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Table Sparsification parameters. The model is trained from scratch on HumanML3D [31]. The right arrow + means that the closer to the real motion, the better. Bold indicates the best result.
Method
AIT(s))
R Precision 7
FID
MM Dist.
Diversity
MModality?
Top 1
Top 2
Top
Real
0.511
0.703
0.79744002
0.0024.000
2.974
9.503+.080
Full Attention
0.061
0.565 .002
0.759
0.850
0.0334.004
2. 2.708
9.5984.062
2.833 043
3 0.75 0.93
0.014
0.466
0.652
0.7474.000
0.644
3.316+t.007
9.638
1.073 042
3 0.75 = 0.95
0.016
0.517#5005
0.691
0.79g
0.305 o0s
3.035+.000
9.745
2. 2.653 025
3 == 0.75 y= 0.97
0.019
0.538
0.723
0.819
0.097
2. 2.73.tt.00
9. t..5
2. 2.707 052
3 0.50
0.93
0.023
0.515 001
0.690 008
0.792 009
0.065 004
2. 2.752 o05
9. 9.598t-002
2.619 071
0.50 7 0.95
0.027
0.562
0.754
0.847 4.847
4-002
2.711 4:000
9. 26.1
2.812
== 0.50
0.97
0.035
0.559
0.752
0.841
0.0:00
2.713
9.142
2.820
3 0.25
0.93
0.042
0.545#.005 000
0.742 002
0.810
0. 0.1074.000 o00
2.9015+.004
9.465 435
2.364 046
3 0.25
0.95
0.048
0.560=.002
0.753
0.846+.001
0.040
2.711
9.657#.053
2.819#.085 035
3 = 0.25
0.97
0.053
0,562 .05
0.753 002
0.845
0.039 4.00
2.715
9.633
2,826 063
Table 4: Model configurations. The model is trained from scratch on HLmanML3D [31]. The right
arrow means that the closer to the real motion, the better. Bold and underline indicate best and second best results.
Method
AIT(s))
R Precision f
FIDL
MM Disr.
Diversity+
MModality
Top 1
Top
Top 3
Real
0.511 +. .003
0.703#.003
0.7974.002
0.002
2.974#0008
9.503+-005
N2 2
0.024
0.532 004
0.732 002
0.8144.002 0002
0.064 003
2.9542.000 .00
9.616t.024
2. 2.426-.006
N₁ = 6
0.032
0.561
000
0.750 006
0.841 000
0.041
0000
2.710#-002
9.785t.068
2.810)tt00
Nr = 8
0.039
0.566 tt 006
0.752 000
0.847 0002
0.0394.000 000
2.709
9.692
2.80
r= 2
0.035
0.542 002
0.745=.000
0.839
0.047##0002 002
2.722
9.684
2. 2.7933t-025
Depth = 4
0.023
0.541 0000
0.7 0.740 001
O. +.03
0.050
2.728##001
9. 9.688 0888#.074
2.807
Depth = 6
0.025
0.35g
0.749 0002
O, t.0
0.045
0002
2.719
9. 9.704
2, 208
Depth= 10
0.033
0.565 003
0.754 00G
0.846-10001
0.038 0001
2.708
9.719
2.811 -L.07
Ours
0.027
0.5624.004
0.754 .005
0.847
0.041
0.0414..022
L.002
2.7114.006
16144t-056
2.812 18122:016
GVP breaks the variance preserving property, which is a theoretical guarantee in score-based and velocity-fiele diffusion training. In contrast, the linear interpolant does not have this issuc.
Sparsification parameters. We conducted experiments with different head ratios 3 and attention thresholds 7, keeping all other settings the same. The results in Table 3 show that increasing the number of Lo-Fi heads and pruning more tokens improves speed but leads to a decrease in perfor- mance. In contrast, our sparsification parameters achieve an excellent balance between efficiency and sparsity, delivering performance comparable to full attention while achieving 2.25x speedup, enabled by unified training and inference without token granularity mismatch.
Model configurations. We investigate different model configuration including number of heads N₁: and model depth of MotionSiT, compression ratio of VAE, compare to our default settings (N₂, 4, r = 4, depth- 8). The results in Table 4 demonstrate the robustness of our model across different configurations. While increasing the model's depth and number of attention heads improves metrics such as FID and MM Dist, it sacrifices efficiency. Our chosen configuration balances performance and efficiency.
5 Conclusion
We present FlashMo, fweqeency-aware sparse motion diffusion framework that addresses both efficiency and scalability challenges in 3D human motion generation. By introducing a unified training and inference sparsification strategy, FlashMo achieves a 2.25x speedup over full attention without c
compromising motion quality. Our proposed MotionSiT architecture leverages geometrically factorized interpolant with Lie group geodesics, enabling principled modeling of joint rotations on SO(3) manifolds. Extensive experiments on HumanML3D, and KIT-ML validate the effectiveness of FlashMo, demonstrating significant improvements over statc-of-the-a methods in both performance and efffccincy. Beyond empirical gains, FlashMo highlights the importance of respecting manifold geometry in diffusion processes, demonstrating that modeling motion trajectories along S3(3) geodesics preserves rotational consistency and yields smoother, physically coherent human motions compared to Euclidean approximations.

---

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16

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Appendix
A Preliminaries
A.1 Scalable Interpolant Transformer (SIT)
Scalable Interpolant Transformers (SiT) [54] is a diffusion transformer [58] based on stochastic interpolants, which defines a class time-indexed stochastic processes that map noise sample Xo ~ Po to intermediate point x(I) over E [0, 1]. These interpolants are defined without requiring a target sample x (as standard DDPMs), and instead use structured noise perturbation with learned dynamics to generate samples.
The interpolant is defined as:
x(t) = a() at)e, c~ c~N(0,1)
where (t) and (t)) are time-dependent scalar functions, Xo is a noise sample (analogous to initial state in forward diffusion). It holds that x(0) Xo. and x(1) idcally follows p1, the data distribution.
These interpolants allow generation and learning without explicitly simulating a forward diffusion trajectory from X1 to as in traditional diffusion models.
Forward SDE. The forward generative process in diffusion models is typically described using stochastic differential equation of the form:
dx = f(x, t) dt + g(t) dw.,
where f(x, f) is drift function, g(t) is diffusion coefficient, w. is standard Brownian motion.
Reverse-time SDE. The reverse- time dynamics of this process can be derived from the theory of time-reversal of stochastic processes. The reverse time SDE is:
dx
[f !) v(t)²V (x)] dt + g())dw,
where Vx log (x) ) is the score function, which denotes the gradient of the log-density at time t, dw, reverse-time Brownian motion.
This reverse-time SDE shows that, to sample from the data distribution starting from noise, we need to access the time-dependent score function of intermediate states. This is typically learned using score matching in diffusion models.
Probability flow ODE. An alternative, deterministic formulation of the same marginal distributions given by the probability flow ODE:
dx
1 ((x,)) ( (x))
di
Unlike the reverse SDE, this ODE yields a deterministic mapping from X to X1. Critically, both the reverse-time SDE and the probability flow ODE share the same marginal distribution (x) for each
This connection allows one to model generation either stochastically (via sampling the reverse SDE) or deterministically (via integrating the ODE). SiT, the idea is to sidestep direct score estimation and instead predict the time-derivative of the interpolant path.
Velocity fields and learning objectives. Rather than explicitly learning score function Vx log (x), SIT models the trajectory of the interpolant x(1) by learning its velocity
dx(t)
vx((),,) dl
Given the analytic form of the interpolant:
x(t) a(t)xo + (t))e
17

---

its time derivative is:
dx(() = ((t)xo + (tt)e.
dt
Since xy and both known (sampled during training), this velocity is analytically computable. The SiT model learns a velocity estimator vo(x(!), 1) by minimizing the expected squared error between the predicted and true velocity:
(()) = Exp,c [.llbatcc0000 dx(t)
dl
di
This learning objective removes the need for score estimation denoising objectives and allows SiT to scale better.
Interpolants design. The choice of interpolant functions at)), the deterministic scaling of the input noise Xg and (t), the time-dependent standard deviation controlling the magnitude of the stochastic perturbation directly determines the geometry of the stochastic path x(t) = a(()xxo + (t)e. This, turn, influences training dynamies, sample quality, and generalization. Below, we describe two common interpolant designs that capture different trade-offs
The linear interpolant is defined as:
a(t) =1 (t) Vt.
This design induces linear interpolation in the input Xo and square-root scaling of the noise. At t = 0, we have x0) = Xo; at 1, a(1) = 0 and (1) 1, hence x(1) = e, a pure noise sample.
This interpolant is simple and intuitive, but its marginal distribution px(x) varies in both mean and variance across time. Specifically:
E| [[x(()] ([xo = O, Var[x(0] a(1) + t(12 =1 2)2+ +
Thus, the total variance is üimevvarying
To address the variance inconsistency, the GVP interpolant is constructed such that the total variance remains constant over time:
a(1)² + +o(2==
A canoncial choice under this constraint is:
axt) cos
π a(t) = sin (
2
This design ensures that:
x(t) (O,, Vt [0, 1].
That is, the marginal distribution of x(t) stays isotropic Gaussian throughout the path. This simplifies score estimation and enhances training stability. Moreover, the smooth transition from Xo to noise is nonlinear, leading to smoother gradients and more coherent sample trajectories.
The choice between them depends on downstream task requirements and model capacity.
A.2 Lie Groups for Rigid Body Rotations and Motions
Rigid body rotations and transformations in three-dimensional space are not elements of Euclidean space, but instead belong to structured -un-Euclidean manifolds with group structures, specifically Lie groups. This geometric structure is critical for ensuring mathematically consistent operations such as interpolation, averaging, and noise perturbation, which are frequently needed in motion analysis and generation tasks.
The Special Orthogonal Group SO(3). The space of all 3D rotation matrices forms a Lie group known as the special orthogonal group:
SO(3) {R e R 3x3
R I, det (R) = =
which a compact, connected, non-commutative Lie group of dimension 3. Each element of SO(3) represents a proper rotation in R and the group operation is matrix multiplication. The non-Fuelidean nature of (O(3) implies that standard linear operations, such as averaging two rotation matrices interpolating between them, may lead to results that no longer lie on the manifold.
18

---

The Special Euclidean Group SE(3). For full rigid body transformations, including both rotation and translation, the appropriate Lie group is SE(3):
R
t 1
SB
*x4 RESO(3), L R
which is a 6-dimensional, non-compact, non-commutative Lie group. The group operation again matrix multiplication, and SE(3) encapsulates both orientation and position of a rigid body in space.
Lie Algebras and Local Coordinates. Associated with each Lie group & Lie algebra g. which serves as the tangent space at the identity element and provides a local, linear coordinate system for the manifold. For (3), the Lie algebra so(3) consists of all 3D skew-symmetric matrices:
so {A E R 3x3
A -
A standard isomorphism between R and so (3) is provided by the hat operator:
w3
W2
[
x
Wy
W1
WER
W1
which maps vector to its corresponding skew-symmetric matrix. The inverse operation is the vee operator, mapping from so(3) to R.3
Exponential and Logarithmic Maps. The exponential map exp - G and its inverse, the logarithmic map log + g. provide the tools to move between the manifold and its tangent space. For SO(3), the exponential map is given in closed form by Rodrigues' formula:
sin [
1 cos
exp([w)x) = I+
+
[
θ = ||w|].
g2
This constructs rotation matrix corresponding rotation of angle 0 around axis w/|lw/] The logarithmic map inverts this operation, computing the minimal-axis rotation vector w corresponding to given rotation matrix R:
(T(RR) - 2
log( Rt) 2 f (R RT),
0 = cos
Interpolation and Perturbation on Lie Groups. A major benefit of the Lie group structure is that interpolation and noise perturbation can be carried out in the tangent space, ensuring that the results lie back on the manifold after mapping. Given two rotations R₁ R2 E SO(3), geodesic interpolation can defined via:
R(t) = R2 exp (t log(R) R2)), E [0.
which traces the shortest path on the manifold between Rj and R2. More generally, any operation of the form:
R exp(w), w ~ N(0, E),
defines a distribution on SO(3) by sampling from a Gaussian the tangent space RS and mapping to the manifold via the exponential map. Such constructions are widely used in manifold-aware generative models and motion synthesis.
Extensions to SE(3). For rigid body motion in SE(3), the corresponding Lie algebra sc(3) is 6-dimensional space encoding translational and rotational velocities. Elements of se(3) are typically expressed using twist coordinates (u, w)) E 6 and the exponential or logarithmic maps general- ize accordingly. The caaerrCammbellaaaaeeee Hausdorff formula governs the non-linear composition of transformations, and matrix representations of exp and log are available via the theory of screw motions.
This Lie group machinery forms the mathematical foundation for handling rotation and transformation data in consistent, geometry-aware manner, and indispensable in domains such as roboties, graphics, and motion modeling.
19

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B
User Study
This study conducts comprchensive user evaluation of our method compared with MotionLCM [17] and MoGenTTS [85]. We assess the real-world applicability of motion sequences generated by Motion Anything and baseline models using Google Forms survey completed by 50 participants. As shown in Figure 7, the user interface presents 3-4 motion clips (Videos 1 1-3/4) generated by the same model, followed by comparative set (Videos A-C) from different models. Participants evaluate each animation based on motion accuracy and overall user experience, using 3-point scale (1 low, 3 high). In the comparison section, users select the model they perceive as most realistic and engaging. This evaluation is designed to measure both the fidelity of the generated motion to real-world human movement and the overall effectiveness of each model in delivering visually compelling results.
Results:
Our method achieved a motion quality rating of 2.92, with 94% of participants agreeing that produces high-quality motion with minimal jitter, sliding, unrealistic artifacts.
For motion diversity, received rating of 2.86, with 90% of participants indicating that our method generates complex and varied motion sequences.
In terms of rext-motion alignment, our model scored 2.80, and 82% of users reported that the generated motions were well-aligned with the given text descriptions.
Notably, % of participants preferred our method over competing approaches.
C Qualitative Results
To qualitatively evaluate our performance in text-to-motion generation, compare the visualizations generated by our method with those produced by both state-of-the-art diffusion and VQ-VAE based methods specializing in text-to-motion generation, including MotionLCM [17] and MoGenTS [85]. The text prompts are customized based on the HumanMLLDD [31] test set. As shown in Figure and video demos, method generates motions with superior quality, greater diversity, and better alignment between text and motion compared to the previous state-of state-of-the-an f-thh-arrt methods.
D Full Comparison Tables
To comprehensively evaluate our method on text-to-motion generation, report full comparisons with prior approaches in Table 5 and 6, Our method consistently achieves state-of fotte-of-th--ee the--rr-tt performance on both HumanML3D [31] and KIT-ML [61], outperforming existing baselines across all metrics.
E Broader Impacts
Our work advances the field of 3D human motion generation by addressing key limitations in efficiency and scalability that hinder real-world deployment. By introducing frequency-aware sparsification and scalable transformer architecture with principled geometric modeling, FlashMo offers a more practical solution for generating realistic human motion at scale. This has broad implications for downstream applications such as human-robot interaction, AR/VR environments, animation, and digital avatars. By reducing computational demands without sacrificing quality, approach lowers the barrier adoption in cooource-constrained settings and paves the way for real-time, interactive, and embodied Al systems.
Limitations and Future Work
Currently, our design has not yet explored other interpolant functions, and still rely learning latent diffusion models on the tangent space. In future work, we will investigate how different interpolant forms benefit cemporal-spatio factorization and Lie group geometric interpolation for improved motion modeling. Moreover, we will continue to investigate manifold geometry in motion generation from an optimization perspective.
20

---

Table 5: Comparison of text-to-motion generation on HumanML3D [31] dataset. + indicates the closer to real data, the better. Bold and underline indicate best and second best results. Efficient motion diffusion models are highlighted in blue.
Method
Veme
A
R-Precision
HD J
MM Diat J Deverityy
MModalaty
Top:1
Top-2
1op-3
Real
(.511
0.70g
0,797
0.002
2.97
9.503
TMET 1321
BCCV 2022
0.760
0.024 1o0
0.618
0.728 805
1.001 D17
3.167
8.589 Dn
2. 2-024 1oG
T2M
CVPR
0,380
0.09224.00 cm
002
9.02
0.141
3.121 8.000
9.722 .e0
1.831 ouB
CoMo [39]
BCCV 2024
0.620
0.5024.00
0.602
0.7908.002
0.2624.004
3.032 8.03
9.936
1.015*
MMM [60]
CVPR 2024
0.081
0.504
0.6 0.606-100
0.7949.022
0.0897-00s
2 2.008
9.411
1
McMask [10]
CVPR 2014
0.120
0.521#:000
0.7 0.713=
2.868 0.045*-10u 0.8072/012
1 1,241
BAMM
ECCV
0.525 00
0.720
0.8142.05
0.005 Fe0
2.919 Aas
9.717 160
1
MotieaTS [85]
NeurtPS 2024
0.181
0.522 mm
0.71y
0.8125 903
0.00pt ne1
2.867 nos
9.57y ore
MDM [70]
ICL8 2023
24.74
0.327t
0.49y=
0.544 Re
5.0666 077
9.5
2. 79gg
MotionDiffuee [93]
TPAMI 2024
14.74
0.291
0.081
0
0.622++-000
1,113
9.410
1.50g
MLD [7]
CVPR 2023
0.217
0.08]
0.673
0,772
0.4734.003
3.196
9.72g
2. 2.413*
ReMoDilluse [91]
ICCV 2023
0.510
0.608 100
0.796
0.1000..004
2.97
9.018
2.03
M2DM [64]
JCCV 2023
0.237
0.082-002
0.703
0.30g4.0ec
3.13
9.926
3.587
Fg-T2M [T3]
ICCV 2023
0.202
0.683
0.783
0.243*
3.109
9.278
1.G14
MineMoCien
NewtPS 2023
0.504
0:630
0.78
0.151 nes
2.00 Ais
9,253
2. 1BA9
GraghMotion [42] (50-step)
NevrIPS 2023
0.776
0.2964.00
0.086
0.118 0.778
3.147
9.796 000
GraghiMation [42] (150-step)
NeurIPS 2023
2,552
0.504
0.600
0
0.116*t.007
3.070 3.070
9 0.692..00
2. 7.766
B2A-HDM [30]
2AA120224
(.511
0.
0.791
0.084 nou
3.12 n10
9,526
1 1.
M2D2M [12]
BCCV 2024
0,79ga.802
0.087 004
3.015 Aos
9.672
2.1 2,115
MotionLCM [171 (1-sep)
ECCV
0.502
20002
0
0.4674.002
3.022 8.022
0.000
Motionl.CM [17] (2-step)
BCCV 2024
0.035
0. 1.505-mc
uc
0. 7
0.805
0.568
0.600-002
2.
Maxionl CM [17] (4-step)
RCCV 2014
0.043
0. 0.502
0.608
0.7062.902
0.5012t000
3.
0.607
2. 2.25
EMDM [104]
ECCV 2024
0.050
0.008
0.68 100
0.786
0.11244.00
1.
9 2
1,841
Motian Mamba [99]
ECCV
0.502-.00
1022
0.792
0.28p
066
Stuhl-MoFasionn [38]
MM
0.489
0. 0.553+ ue
0.00
0.748 MM
1.
MotimLLMM V2 [16] (1-te))
Prepeint 2024
0.631
0.556
0.7
0.837
0.0724.000
2. 2.767
9.5774.0m
1 1.85
MotionLCMM- V2 (2-step)
2024
0.501 00
0.04
2.700 1955
9.58
1. 1.
MotionLCM V2 [16) (4-step)
Preprint
0.5534.00 mo
0.056 4.92
2.77a .033
9.59g -.07
MMDM-1 [8]
Preprint 2024
0.284
0.
0.754
0.319
R23
0.2992.004
2, 7.74 1-112
MMDM-b [8]
Preprint 2024
0.055#-
0.627
0.703
0.2855t-00
3. 3
0.508
2. 7070
FTMoMamha [47]
Prepeint 2024
0 0.489+
0. 6808
0.7772/012
0.181
3.151
9.78p
2. 2.277
Light-T2M (89]
AAAI 2025
0.511 oo
0.638
0,796
0.040 nen
3.002 (01
1,877*
Free-MDM [6]
Preprint 2025
0.045
0.605
0.657
0.757 0.757-905
0.25g+t145
0.695-.00 t8a
Free StableMoFassonn [6]
Propeint 2025
0.036
0.52g
0,707
0.00 2.00
0.05144000
9,490
MotionPCM (1-5t))))))))
Preprint 2015
0.702
0
0.044 nee
2,711
2.005
9.5
1.772 FMT
MotionPUM [4:] (2-step)
Prejeint
00006
0.505 me
0.7
0.003+ nee
007
9.01g n69
1.70g+ coa
MotimpPMM [4]] (4-stpp)
Preprint 2025
BJD46
0.5tg
0.702
D.8422.91
2,716
9.576 A.n
1.714
Fg-12M-+ [74]
DCV 2025
0.51gh
0.7122
D201
0.089t
2.525
9.223
2 2.625
RioMaDifllse
Propeint 2025
0.5474.00
0.743= 1012
0.6362.012
0.07
2.784 1955
9.507
1,019t.00 tess
HiSTF Mamba [90] (10-step)
Prepeint 2025
0.000
0.28yt no
0.085
0.7842-915
0.189+ mn
3.101
9.712 me
2. 644
HiSTF Mamba [90] (15-step)
Pregeint 2025
0.50p
0.798
0.219 res
3.05g
9.28gh
2. 2.277
ACMo [70]
Propeint 2025
0,796
0.102
2.977
9.749
2,814
FlashMo (Ours)
4o00 ros
0.75
0.847
0.041
2,711 1906
9.614
2.812
FlashMo w/ pretrain (Ours)
0.027
0.56g mr
0,761
0.851
0 160
2,703 1055
9.601 oms
2,85 (00
21

---

Section
Section 1.2
first questio
provided second
question please shoose best mation from motions.
Seation 1.1
Section
The sim appears wakk bowaad bend sligtly grabting an object with their hand
section1 2.2
sptining
Section
starts dance lot
section1 2.3
Quession
quality
generations Speciically
Which video you belleve that has the best motion quality?
whether they wshbltt notionate jibee, sliding unvealitic movement and select the staemmnt that best reflects your evaluaioon.
riscelization acceptable
aspects that need
The disvlization is stong highiighting the potential impart and pramising foture for applications
Question
QuesSor22
Which video believe that has the highest diversity?
Please evaluaee the average diversit of the motion genorations provided and statement reflects
The motions exhbtt moderase diveraty, oerain eiements predittable
highly divers, shoncasing range movement putterss
Question
Queptio22
corresponding motions previded, option your assesament
forwards streight while stumbling"?
algement between mosions
The sexts and motioss alinn faiyy well though some detals inconsiseenies remain.
wel--ligned accuratel refecting
0
Figure 7: User study Google Forms. The User Interface (UT) used in our user study.
22

---

Text
MotionLCM CM
MoGenTS
Mo
The man appears to walk forward bend slightly grabhing an object with their left hand.
A person spinning with is spread out and then falls over.
The man starts dance lot,
MARSSS
person walks forwards straight while stumbling.
A person does karate by striking with their hands and kicking their legs.
Figure 8: Qualitative evaluation on HumanML.3D [31] test set. We qualitatively compared the visualizations generated by our method with those produced by MotionL.MM [17] and MoGenTS [85].
23

---

Table 6: Comparison of text-tommotion generation on KIT-MI. [61] dataset. -> indicates the closer to real data, the better. Bold and underline indicate best and second best results. Efficient motion diffusion models are highlighted in blue.
Method
Precision
FID
Dist
Diversity + MModality
Top-1
Top-2
Top-3
Real
0-124=
0,649
0.77g
0:031 004
2.78g
11,06
TM2T [32)
BCCV 2022
0,288
100
0,4635/00
0. 0.587 0rG
3.50g--155
1.501
9.17844117
3. 3,2924 051
TZM-GPT [92]
CVPR 2023
0.4166-100
0.416
0.6274.000
0.745
0.5144-02
3.007
10.924-100
1 1.570-4
CoMo [39]
BCCV
0.122
0.63
0.7
0.332
2.873#-023
10.96+-100
MMM [60]]
CVPR 2024
0.404
0.621 0.6211.000
0. 0.744
0.316
2.977 mn
10.91 1.300
1 1.232 029
MoMas& [30]
CVPR 2021
0.133 907
0.656
0.781
0. 0.201
2.779
1.131 043
BAMM [59]
BCCV 2024
0.438
0.06] 1:000
0.788 ots
0.183 o01
723- m7
11.01 4.004
1 6.660 015
MoGenTS [85]
NewrIPS 2024
0.445
0.671
0.797
0. 0.14g 4-04
2.711
10.92
MDM [20]
ICI.
0.164
0,291 5:004
0.396 0004
0.497
9:191 2-1122
10.85 155--110
1.907 234
MotionDiffuse [93]
TPAMI 2024
0.417 0.417-101
0.621
0. 13739
1.954
2: 2.988-
11,10
0. 0.730-4
0.390
0.609
0.731 007
10.80 10.80
2.182 471
RoMoDiffus [94]
ICCV 2023
0.127=.014
0.64)
0. 0.7655
0. 0.155
2.51 2.81.
10.80 10.80
1 1.239
M2DM [44]
ICCV
0.405 000
0.629 203
0.739
0.502 o4D
3.012 105
11,38
3.273 045
Fg-T2M [73]
ICCV 2023
0.418
0
0.7 000
0.571
10.88 10.88
1. 11.01 029
HineMoGen [95]
NewrPPS 2023
0.432 -.432-1002
0.649
0.772
0.178 0.178-1007
10.85-4-115
1 1.777
GraphiMotion [12] (50-stop)
NeudIPS
0.117
#:000
0.755 000
0.202
3.085
#:100
132
GraphMotion [42] (150-step)
NeurIPS 2023
0.429 -007
0.648
0. 0.789
0.513
3.076
11.124.335
3.627
B2A-HDM [80]
2024
0,130 100
0.653 200
0.773 00G
0.307 020
2. 024
10.86 124
047
M2D2M [12]
ECCV 2024
0.753
0.378
3.012..mm
10.71
2.061
EMDM [104]
202V
0.413
00660
0.780
0,281 0
2.57
10.96+t008
1 1.315
Motion Mamba 199]
ECCV 2024
0.419 1os
0.645--900
0.
o06
0.307
3.021 ms
11,024
1. 6.665 014
StableMoFusion [38]
MM 2024
0.445
0.860
0. 0.782
0,258 0,258410z
10.94
1 1.502
MMDM-1[8]
Preprini
0.432 100
0.643 200
0.780 006
0.237: o1s
9393 on
10.84 135
1/457
MMDM-5 [S]
Prepriat 2024
0.386
0.603
0.729
0.408
3.215
100
2 2.261
Light-T2M [89]
AAAI 2025
0.114
0.670
0.79
0,161 000
7.
1 1,005
Hcc-MMDM [6]
Preprint 2025
0.382 1000
0.687 1.06
0. 1 007
0-401 1.00
10.73 1..73
Free- Siee-StullMMoousio [6]
Preprint 2025
0.431
0.671
0. 0.799
0. 0.15
10.50
MationPCM (4tep)-step)
Preprint 2025
0.435=.007
0.654
0.78
0.355
2.820
10.78
1.537 047
MationPCM [41] (2-step)
Preprint 2025
0.437=.00
0.664
0. 0.787
0:20442001
2.844
10.89 004
1 1.254
MationPCM [41] (4:step)
Preprint 2025
0.443
0.664
0.78g
0.3
2.891
10.76+0000
1 1.258 050
Fg-T2M++ [74]
IIC 2025
0.412
0.657 200
0.781
0. 0.55 004
2.09g o11
10.96
1 1.255 078
BioMeDifluse [43]
Preprint 2025
0.448=.000
0.666
0. 0.78
0.211
2.772
11.11
1 1.550* 050
HiSTE Mamba [90] (10-step)
Preprint
0.437 1o0
0.651 4.006
0.772 mo0
0:280 1.13
ms
10.92 L.06
0s8
HiSTF Mamba [90] (15-step)
Prepeint 2025
0.110
0.657
0.77
D. 2.2997.017
2.819
10.98
1 1,517
FlashMo (Ours)
0.449 1012
0.670
0.799
2000
10064
3.287 1942
FlashMo w/ pretrain (Ours)
0.453
0.679
0
0.132
2.701
10.75
3.191
24

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31


Reviews:
[{"review_id": "9FG79DkVZD", "reviewer": "Reviewer_8uxk", "summary": "This paper presents FlashMo, a text-to-motion (T2M) generation approach that incorporates frequency-aware information. To enable an efficient backbone for both training and inference, the authors extend the Xt interpolation in flow-matching ODEs, which are commonly used in the image domain's Euclidean space, into the realm Lie group which is used to present body joint rotations. The authors also  introduce a new model called MotionSiT. Furthermore, they propose a pruning strategy for the attention score matrix based on Hi-Fi and Lo-Fi frequency patterns in the final feed-forward layer, aiming to improve training efficiency while preserving realistic, frequency-aware motion details. The model is pretrained on the large-scale MotionHubV2 dataset and evaluated on HumanML3D and KIT-ML, showing superior quantitative performance compared to existing T2M methods.", "questions": "The description of the \"frequency-aware\" head partition in Section 3.3, as well as the procedure for generating Figure 4, is unclear. Specifically, it is not well explained how the frequency patterns are computed or how attention heads are categorized into Hi-Fi and Lo-Fi. Additionally, the caption of Figure 4 mentions \"100 motion features,\" but it is not specified what exactly these features represent. Clarifying these details would help readers better understand and reproduce the analysis.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength\n\n(1) The idea of incorporating the interpolation process to the SO(3) manifold is well-motivated. Since human motion is inherently represented through rotations, modeling it in Euclidean spaces is fundamentally inappropriate. Traditional approaches rely on Euclidean gradient projections to approximate the target, rather than walking on the geodesic on the rotation manifold directly. By operating directly within SO(3), the proposed method aligns more naturally with the underlying structure of motion data.\n\n(2) The pruning of attention score matrix for the Lo-Fi heads efficiently speeds up training.\n\nWeakness\n\n(1) The sparse multi-head attention is not sufficiently motivated or evaluated. While the authors integrate a Lie Group interpolant with sparse attention, no ablation studies are provided to isolate and quantify the contribution of each component to overall performance. Although the frequency-aware sparsification is conceptually well-grounded based on observed patterns in attention heads, it remains unclear whether alternative designs would lead to motions with different frequency characteristics. Additionally, it would be valuable to understand whether models employing frequency-aware pruning exhibit more realistic dynamics or richer frequency details compared to those without it. Some qualitative results or visualizations could help clarify the rationale and effectiveness of this design choice.\n\n(2) The evaluation of the proposed framework is somewhat unclear. In Table 1, FlashMo (ours) without pretraining appears to be trained solely on HumanML3D or KIT-ML. However, despite the strong performance of FlashMo w/ Pretrain, the non-pretrained variant does not outperform existing methods such as MotionPCM or MoGenTS. Given that MotionHubV2 is described as a large-scale dataset, I assume it contains a richer diversity of motions compared to HumanML3D, which raises the question of whether the final performance gains stem primarily from the proposed technical innovations or from the benefit of pretraining on a much larger dataset. This distinction is important for assessing the true contribution of the method itself. Additionally, I found no public access for MotionHubV2 for now which also hinders reproducibility.", "was_revised": true, "final_justification": "I appreciate the authors’ efforts and have carefully read all of their responses. I think they addressed my concerns well so I will raise my score.", "rebuttal": "We appreciate the reviewer’s valuable feedback and reponse to the concerns as follows.\n\n**Q1.1: Motivation of sparse multi-head attention.** \n\n**A1.1:** Our motivation of sparse multi-head attention is recognized by Reviewer GZdj, “The frequency-aware sparse attention mechanism is well-motivated…”.\n\nFuthermore, in our paper, the motivation of sparse multi-head attention has explicitly mentioned in **lines 28-31** “Efficiency. Motion diffusion suffers from high computational cost, as well as long training and inference times. Existing efficient methods such as step distillation [1,2], step reduction [3], and linear models [4,5] either require additional training of teacher models or involve complex scanning mechanisms, resulting in overengineering and low efficiency.” and **lines 37-40** “We observe that dynamic motion, which is more important, exhibits higher frequency compared to static motion in generation process, as shown in Figure 1. Hence, we design a frequency-aware sparsification mechanism that dynamically prunes tokens corresponding to low frequency motion, enhancing efficiency while preserving high frequency motion at the attention head level.” \n\n**Q1.2: Ablation study.** \n\n**A1.2:** The ablation study of each component including geometric factorized interpolant and frequency-aware sparsification have shown in Table 2 and 3. \n\n(1) In Table 2, our interpolant method significantly improves generation quality compared to other methods. \n\n(2) In Table 3, our frequency-aware sparsification significantly improves efficiency while largely preserving generation quality compared to the full attention baseline. \n\nBoth generation quality and efficiency are non-negligible perspectives of overall motion generation performance, which is recognized by Reviewer uEGj, “This enables faster computation while balancing with quality”.\n\n**Q1.3: Frequency-aware sparsification for different architecture.** \n\n**A1.3:** Sparsification methods are often designed for a series of models with similar or shared architectures. e.g. Sparse VideoGen [6] introduces a semi-structured sparsification method by designing a predefined attention mask based on the observed attention patterns in CogVideoX [7] and HunyuanVideo [8], both of which share a common video DiT architecture. \n\nMoreover, our frequency-aware sparsification is a structured sparsification method with adaptive token selection, without predefined attention masks, which expands the potential of our method to adapt to other architectures.\n\n**Q1.4: Exhibit more realistic dynamics or richer frequency.** \n\n**A1.4:** This question relates to the fundamental mechanism of token sparsification methods, which prune tokens from the original set, reducing redundant and less important information without introducing new content [9,10], i.e., token sparsification methods aim to improve efficiency while preserving important information as much as possible (which impacts generation quality); they do not enrich motion frequency or exhibit more realistic dynamics.\n\n**Q2.1: Evaluation and concerns about w/o pretrain.** \n\n**A2.1:** In Table 1 and Figure 6 of our paper, the performance of FlashMo w/ pretraining is to demonstrate the outstanding scalability of our method, which does not hinder the performance gains achieved through our method’s advancements. \n\nIn Table 1, FlashMo (**without pretrain**) achieve SOTA in 5 out of 8 metrics in HumanML3D and 4 out of 7 metrics in KIT-ML, while MotionPCM achieves SOTA on at most two metrics (1-step) and MoGenTS does not achieve any SOTA on HumanML3D, similarly, MoGenTS achieves SOTA on only two metrics and MotionPCM does not achieve any SOTA on KIT-ML. \n\nThis has been widely recognized by Reviewer GZdj, 'The proposed method shows better performance and becomes more efficient,' and by Reviewer HnUJ, 'The proposed method achieves state-of-the-art performance with the lowest inference time, training time, model size, and GFLOPs.’\n\n**Q2.2: No public access for MotionHub V2.** \n\n**A2.2:** The MotionHub V2 dataset is publicly available on the GitHub repository of MotionLLAMA [11], provided via a BaiduPan link.\n\n**Q3: Frequency-aware head partition and figure 4.** \n\n**A3:** As defined in **lines 156–158** of our paper, attention heads are partitioned based on their frequency into Hi-Fi and Lo-Fi groups, where β is the partition ratio, which is ablated in Table 3. \n\nAs for the procedure for computing the frequency pattern in the figure, the caption of Figure 4 in our paper mentioned: 'The frequency magnitude is computed using the Fast Fourier Transform (FFT) and averaged over 100 latent motion features’. Specifically, the frequency magnitude of each attention head is calculated with a Fast Fourier Transform (FFT). We take the output feature maps from either the Hi-Fi or Lo-Fi attention head. For each feature map, we apply FFT to convert the latent representation into the frequency domain. We then compute the magnitude of the frequency components and apply a logarithmic transformation for stability and better interpretability. To ensure a reliable estimate, we randomly select 100 latent motion features and average their frequency magnitudes. This averaged result reflects the typical frequency characteristics of each attention head and is used to analyze or visualize the frequency sensitivity of different groups. Adapting FFT to calculate frequency magnitude is a common approach, and similar methods have also been introduced in works such as [12] and [13].\n\nOnce again, we appreciate the reviewer’s valuable feedback. We kindly hope you will consider raising your score if we have adequately addressed your concerns.\n\n**Reference:**\n\n[1] MotionLCM: Real-time controllable motion generation via latent consistency model (ECCV 2024)\n\n[2] MotionPCM: Real-time motion synthesis with phased consistency model.\n\n[3] Emdm: Efficient motion diffusion model for fast and high-quality motion generation (ECCV 2024)\n\n[4] Motion mamba: Efficient and long sequence motion generation. (ECCV 2024)\n\n[5] Light-t2m: A lightweight and fast model for text-to-motion generation (AAAI 2025)\n\n[6] Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity (ICML 2025)\n\n[7] CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer\n\n[8] HunyuanVideo: A Systematic Framework For Large Video Generative Models\n\n[9] Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers (CVPR 2023)\n\n[10] PACT: Pruning and Clustering-Based Token Reduction for Faster Visual Language Models (CVPR 2025)\n\n[11] MotionLLaMA: A Unified Framework for Motion Synthesis and Comprehension (also known as “VersatileMotion”)\n\n[12] Improving Vision Transformers by Revisiting High-frequency Components (ECCV 2022)\n\n[13] Parameter-Inverted Image Pyramid Networks for Visual Perception and Multimodal Understanding (TPAMI 2025)"}, {"review_id": "SM005KPDAA", "reviewer": "Reviewer_GZdj", "summary": "This paper proposes FlashMo, an efficient and scalable 3D human motion generation framework to deal with high computational cost and limited scalability due to the U-Net inductive bias. The authors propose a frequency-aware sparse attention mechanism that dynamically prunes low-frequency motion tokens without requiring custom kernel implementations. They also design a transformer-based backbone, MotionSiT, which incorporates a geometric factorized interpolant using Lie group geodesics over quaternion manifolds, enabling accurate and scalable modeling of joint rotations. Experiments on HumanML3D and KIT-ML, show that FlashMo outperforms previous methods in both motion quality and efficiency.", "questions": "1. Can you explain why your method outperform the Real data in R precision?", "limitations": "There is no obvious negative societal impact of their work.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The frequency-aware sparse attention mechanism is well-motivated, practical, and integrates seamlessly with hardware-optimized exact attention.\n2. MotionSiT decouples temporal and spatial dimensions and applies lie group geodesic interpolation, which enhances the structural integrity of motion sequences.\n3. The proposed method shows better performance and becomes more efficient.\nWeakness:\n1. In Figure 2 (d), the Guide Token Pruning should be detailed in the figure for easier understanding.\n2. In the ablation study, the performance undergoes significant changes with slight variations in \\gamma. Does this indicate that the method is not robust to hyperparameters? Additionally, are the same parameters applicable to another dataset?", "was_revised": true, "final_justification": "The authors have addressed my most concerns, so I lean to accept this paper.", "rebuttal": "We appreciate your valuable feedback and support. Our responses to the questions are as follows.\n\n**Q1: Figure 2(d) details.** \n\n**A1:** As shown in Figure 2(d) and Section 3.3, the input tokens 𝑋 are first projected into queries 𝑄 and keys 𝐾, which are used to compute attention scores. These attention scores indicate the importance of each token and guide the selection of a subset of tokens 𝑍, where low-importance tokens are pruned. This adaptive token selection reduces redundancy while preserving key information for subsequent attention computation. We will include a more detailed figure in the next version of the paper.\n\n**Q2: Method robustness to the hyperparameter γ.** \n\n**A2:** Thank you for bringing up this important question. The attention threshold γ is related to the number of adaptively selected tokens, as mentioned in Section 3.3 (Line 173). In token sparsification, model performance is sensitive to the number of pruned tokens, and pruning more tokens leads to greater information loss, which the similar pattern can be observed in both vision and language models [1]. Hence, sparsification methods aim to prune redundant tokens while preserving as much performance as possible. \n\nThis exactly demonstrates the benefit of our adaptive token pruning, which significantly reduces information loss compared to methods using a fixed number of pruned tokens (e.g. predefined attention mask, or hard token threshold). This advantage can be also demonstrated in the experiment on KIT-ML in the following table, where the same sparsification hyperparameters remain applicable, which verify the method robustness to our hyperparameters.\n\n| Method | AIT(s) ↓ | R Precision ↑ (Top 1) | R Precision ↑ (Top 2) | R Precision ↑ (Top 3) | FID ↓ | MM Dist ↓ | Diversity → | MModality ↑ |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Real | - | 0.424 ± .005 | 0.649 ± .006 | 0.779 ± .006 | 0.031 ± .004 | 2.788 ± .012 | 11.08 ± .097 | - |\n| Full Attention | 0.065 | 0.451 ± .003 | 0.677 ± .003 | 0.805 ± .004 | 0.141 ± .006 | 2.705 ± .044 | 10.69 ± .062 | 3.431± .058 |\n| β = 0.75 γ = 0.93 | **0.016** | 0.411 ± .002 | 0.637 ± .005 | 0.759 ± .004 | 0.310 ± .004 | 2.981 ± .057 | 10.55 ± .073 | 1.692 ± .054 |\n| β = 0.75 γ = 0.95 | 0.018 | 0.426 ± .003 | 0.645 ± .003 | 0.766 ± .005 | 0.275 ± .002 | 2.861 ± .044 | 10.46 ± .049 | 2.075 ± .061 |\n| β = 0.75 γ = 0.97 | 0.021 | 0.438 ± .004 | 0.665 ± .002 | 0.790 ± .003 | 0.181 ± .002 | 2.738 ± .050 | **10.84 ± .063** | 2.797 ± .069 |\n| β = 0.50 γ = 0.93 | 0.024 | 0.430 ± .002 | 0.658 ± .004 | 0.785 ± .002 | 0.199 ± .005 | 2.740 ± .039 | 10.69 ± .077 | 2.524 ± .055 |\n| **β = 0.50 γ = 0.95** | 0.029 | **0.449 ± .002** | **0.670 ± .004** | **0.799 ± .002** | 0.152 ± .004 | **2.709 ± .005** | 10.64 ± .074 | **3.287 ± .042** |\n| β = 0.50 γ = 0.97 | 0.036 | 0.447 ± .003 | 0.669 ± .002 | 0.797 ± .004 | 0.150 ± .002 | 2.711 ± .028 | 10.79 ± .056 | 3.279 ± .036 |\n| β = 0.25 γ = 0.93 | 0.044 | 0.435 ± .004 | 0.664 ± .002 | 0.788 ± .005 | 0.164 ± .002 | 2.725 ± .041 | 10.62 ± .042 | 2.986 ± .053 |\n| β = 0.25 γ = 0.95 | 0.050 | **0.449 ± .001** | 0.668 ± .003 | 0.793 ± .001 | 0.150 ± .004 | **2.709 ± .017** | 10.76 ± .058 | 3.286 ± .049 |\n| β = 0.25 γ = 0.97 | 0.056 | 0.447 ± .002 | 0.669 ± .001 | **0.799 ± .003** | **0.148 ± .003** | 2.710 ± .029 | 10.81 ± .040 | 3.285 ± .051 |\n\n**Q3: Outperform the real data in R precision.** \n\n**A3:** This is a classic question. R-Precision evaluates the alignment between a generated motion and its corresponding text description. For each motion sample, a pool of 32 descriptions is created, including the ground-truth text and 31 randomly selected mismatched texts from the test set. The Euclidean distances between the motion feature and the text features in the pool are computed and ranked. If the ground-truth description appears in the top-k closest matches, it is counted as a successful retrieval. The final R-Precision score is the average retrieval accuracy at Top-1, Top-2, and Top-3 positions [2]. \n\nSince the R-Precision calculation relies on the distances in the feature space, in some cases, the generated motion may align more closely with the semantics of the text in the embedding space than the ground-truth motion itself. This could happen if the generated motion is smoother, less noisy, or more \"canonical\" than the real motion, making it easier for the model to match it to the text. As a result, it's possible for the R-Precision of generated motion to slightly surpass that of the ground truth. You can also observe this pattern in other works, such as MoGenTS [3], MotionPCM [4], etc. Hence, in some works, such as MoMask [5], ground truth values are not mentioned in their tables.\n\nOnce again, thank you for the valuable feedback. Your support means a lot and has helped us improve our work. We hope you will consider raising your score if you feel our response has addressed your concerns.\n\n**Reference:**\n\n[1] HiRED: Attention-Guided Token Dropping for Efficient Inference of High-Resolution Vision-Language Models (AAAI 2026)\n\n[2] Generating Diverse and Natural 3D Human Motions from Text (CVPR 2022)\n\n[3] MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling (NeurIPS 2024)\n\n[4] MotionPCM: Real-Time Motion Synthesis with Phased Consistency Model\n\n[5] MoMask: Generative Masked Modeling of 3D Human Motions"}, {"review_id": "2ZZuOhQG7z", "reviewer": "Reviewer_uEGj", "summary": "- Introduces a frequency-aware sparsification mechanism that dynamically prunes low-frequency motion tokens.\n- Using a temporal-spatial factorized interpolant based on Lie group geodesics over quaternion manifolds enables geometrically consistent and scalable joint rotation modeling.\n- Combines temporal-spatial factorization with Lie group interpolation to preserve motion structure and consistency during denoising.\n- Avoids sparsity granularity mismatch, improving training–inference consistency.\n- Seamlessly integrates with hardware-efficient exact attention mechanisms.\n- Achieves high performance on HumanML3D and KIT-ML. Scales well with large-scale data (e.g., MotionHub V2).", "questions": "**I'm a little bit confused about the high-low frequencies in Figure 1**: the attention operates over the tokens in the temporal dimension, so each token represents the entire body at a specific time frame, not individual body parts. However, Figure 1 seems to imply that high and low frequencies are based on specific body parts, which suggests that frequency is defined per joint or region rather than over time.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- **Efficiency and Speed**: FlashMo achieves 2.25× faster inference than full attention without performance loss.\n- **Frequency-Aware Sparsification**: The model dynamically prunes low-frequency motion tokens, preserving high-frequency components at the attention head level. This enables faster computation while balancing with quality.\n- Integrates seamlessly with hardware-efficient attention mechanisms without custom kernel design.\n- Lie group geodesic interpolation on quaternion manifold, more suitable for joint rotations than Euclidean interpolants.\n- **Scalability**: No U-Net inductive bias + Geometric factorized interpolant", "was_revised": true, "final_justification": "The proposed method introduces Frequency-Aware Sparsification, leading to faster inference; however, this improvement comes with a trade-off in quality. The geometrically factorized interpolant appears to improve motion quality to some extent, but the method still falls short of the performance achieved by spatio-temporal masked motion models (MoGenTS) and the phased consistency approach (MotionPCM).\n\nThe results on MotionHub V2 pretraining may not provide a fair basis for comparison; therefore, my evaluation is primarily based on results from HumanML3D. \n\nBased on this, I maintain my initial score, Borderline Accept.", "rebuttal": "We appreciate your valuable feedback and support. Our responses to the questions are as follows.\n\n**Q1: High-low frequencies in Figure 1.** \n\n**A1:** In Figure 1, the frequency of the motion features is mapped onto the motion sequence to better illustrate motion frequency. This visualization shows that motion frequency varies across both the temporal (frame-wise) and spatial (body-part-wise) dimensions. Dynamic frames appear in lighter colors compared to others, and similarly, dynamic body parts are shown in lighter colors compared to static ones. In our paper, frequency-aware sparsification is applied to the temporal attention, i.e., token pruning is performed along the temporal dimension. \n\nTo extend sparsification to both temporal (frame-wise) and spatial (body-parts) dimensions, one could simply adopt a standard temporal-spatial attention structure, where temporal and spatial attention layers are alternately stacked. To verify this, we conducted an ablation study on HumanML3D, which shows slight improvements in both generation quality and efficiency. This is because applying sparsification to both temporal and spatial dimensions provides finer-grained sparsification granularity. These results are not included in the current version of the paper, as the spatial-temporal architecture has been widely discussed in prior works [1,2] and is not the main focus of our contribution. However, we will include them in the next version of our paper.\n\n| Method | AIT(s) ↓ | R Precision ↑ (Top 1) | R Precision ↑ (Top 2) | R Precision ↑ (Top 3) | FID ↓ | MM Dist ↓ | Diversity → | MModality ↑ |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Real | - | 0.511 ± .003 | 0.703 ± .003 | 0.797 ± .002 | 0.002 ± .000 | 2.974 ± .08 | 9.503 ± .065 | - |\n| FlashMo | 0.027 | 0.562 ± .004 | 0.754 ± .005 | 0.847 ± .005 | 0.041 ± .002 | 2.711 ± .006 | 9.614 ± .056 | 2.812 ± .046 |\n| FlashMo w/temporal-sptial | 0.025 | 0.564 ± .003 | 0.758 ± .003 | 0.849 ± .004 | 0.040 ± .001 | 2.709 ± .009 | 9.608 ± .072 | 2.829 ± .051 |\n\nOnce again, thank you for the valuable feedback. Your support means a lot and has helped us improve our work. We hope you will consider raising your score if you feel our response has addressed your concerns.\n\n**Reference:**\n\n[1] MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling (NeurIPS 2024)\n\n[2] Motion Mamba: Efficient and Long Sequence Motion Generation (ECCV 2024)"}, {"review_id": "txIaUz1RLl", "reviewer": "Reviewer_HnUJ", "summary": "This paper introduces method for generating 3D human motion. The key contributions are:\n\n- Frequency-aware sparsification, which prunes low-frequency tokens, improves efficiency without sacrificing performance.\n- A diffusion transformer with temporal-spatial factorized interpolant with Lie group geodesics enables scalable motion diffusion and superior performance.\n\nThe proposed method is evaluated on the HumanML3D and KIT-ML dataset, demonstrating state-of-the-art performance in generating 3D human motion with computational efficiency.", "questions": "1. MotionSiT takes a latent representation $\\textbf{X}$ instead of motion representation $\\textbf{M}$. Temporal-spatial factorized interpolant works well in the latent space and motion space. However, the latent representation does not lie in the manifold geometry unlike the joint rotations. Why the Lie group geodesics are necessary in the latent space?\n2. As I understand, MotionSiT leverages SMPL for motion representation due to the manifold geometry of joint rotations. However, motion representation used in HumanML3D dataset consists of velocities, joint positions, and contact labels. Which motion representations MotionSiT use and how to handle the gap between motion representations?\n\nI will raise the rating if my concerns are solved.", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n\n1. The paper is well written and easy to understand. \n2. Frequency-aware sparsification and considering temporal-spatial structure and the manifold geometry of motion representations are novel and intuitive for motion generation.\n3. The proposed method achieves state-of-the-art performance with the lowest inference time, training time, model size, and GFLOPs.\n4. They validate scalability of their model design compared to other variants and effectiveness of interpolants design.\n\n### Weaknesses\n\nThere is no weakness of the proposed method, but some descriptions of the method are not clear. For specific details, please refer to the following questions.", "was_revised": true, "final_justification": "The rebuttal by authors solve my concerns, so I raise the rating.", "rebuttal": "We appreciate your valuable feedback and support. Our responses to the questions are as follows.\n\n**Q1. Manifold geometry for latent representation.** \n\n**A1:** Thank you for bringing up this question. Below is a clear explanation. \n\nIn order to preserve latent representations on the $\\mathrm{SO}(3)$ manifold, our paper simply adapts a VAE by replacing the standard Gaussian latent space with an $\\mathrm{SO}(3)$-valued latent space and applying a generalized reparameterization trick.\n\nSpecifically, in a vanilla VAE, the latent variable\n\\begin{equation}\nz \\sim \\mathcal{N}(\\mu, \\sigma^2 I)\n\\end{equation}\nlies in Euclidean space and is sampled using the standard reparameterization trick:\n\\begin{equation}\nz = \\mu + \\sigma \\cdot \\epsilon, \\quad \\epsilon \\sim \\mathcal{N}(0, I).\n\\end{equation}\n\nIn contrast, in an $\\mathrm{SO}(3)$-VAE, the latent variable\n\\begin{equation}\nR_z \\in \\mathrm{SO}(3)\n\\end{equation}\n\nis sampled using the following reparameterization trick:\n\\begin{equation}\nv \\sim r(v \\mid \\sigma)\n\\end{equation}\n\\begin{equation}\nR = \\exp(v^\\times) \\quad \\text{(mapped to } \\mathrm{SO}(3) \\text{ via exponential map)}\n\\end{equation}\n\\begin{equation}\nR_z = R_\\mu R \\quad \\text{(left multiplication to center at the mean rotation } R_\\mu)\n\\end{equation}\nwhere $v^\\times$ is the skew-symmetric matrix of $v$.\n\nThis ensures all latent variables $R_z$ lie exactly on the SO(3) manifold without changing the architecture of the VAE. Hence, it’s also necessary to perform lie group geodesics interpolant in the latent space.\n\nSimilar VAE methods have been introduced in previous works such as [1]. Since it is not the main focus of our contribution, the details have not been explicitly included in the current version of the paper. We will include it in the next version of our paper.\n\n**Q2: Motion representation.** \n\n**A2:** HumanML3D introduces a preprocessing step that converts motion into velocity and joint position representations, along with the corresponding text descriptions. However, the original SMPL sequences can be retrieved by locating the exact index in AMASS [2], where the SMPL representation preserves the manifold geometry of joint rotations and is used for training in FlashMo. Moreover, as mentioned in the paper, the output motion sequence needs to be converted to the pose representation defined in HumanML3D to ensure consistency across evaluations and enable fair comparisons with other methods.\n\nOnce again, thank you for the valuable feedback. Your support means a lot and has helped us improve our work. We hope you will consider raising your score if you feel our response has addressed your concerns.\n\n**Reference:**\n\n[1] Explorations in Homeomorphic Variational Auto-Encoding\n\n[2] AMASS: Archive of Motion Capture as Surface Shapes (ICCV 2019)"}]

Author rebuttal:

Meta-review: Strengths
* Applying SiT (scalable interpolant transformer) for motion generation with specific designs
  * Independent noise schedule to the temporal and spatial dimensions
  * Interpolant in the tangent space of the SO(3)
  * Sparse attention separating high-frequency and low-frequency signals

Weaknesses
* Not enough visual results provided
  * Only 5 sample videos
  * No videos on ablations to justify the design choices
* Contrary to the claim of achieving SOTA in numbers, the provided results exhibit discontinuities, jitters, and physical implausibility
* The authors left out an important detail in VAE (in short, the VAE latent code is regularized to be SO(3)) until HnUJ and AC asked
  * AC followed up with a question on simplifying the design by just doing all the learning (VAE and SiT) in the tangent space of SO(3). The author's answer says this is not desirable because of the singularity of the tangent space. This does not answer anything because the same singularity exists in their formulation of using the SO(3) tangent space for the SiT interpolant representation.

While the reviews leaned positively after the discussions, the design choices are not well justified in math and the results. I recommend accepting the paper under the condition that the authors will revise the paper with more convincing reasons why the method needed the SiT interpolant in SO(3), with qualitative ablation studies.
Decision: Accept (poster)