Title: FlashMo: Geometric Interpolants and Frequency-Aware Sparsity for Scalable Efficient Motion Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Motion Generation

Diffusion Model
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

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 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% .

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

[83] and augmented reality [87], game development [21, 101], and video creation [5]. Notably, recent progress in 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-based 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 in 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 frequencyaware sparsification mechanism that dynamically prunes tokens corresponding to low frequency 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 a 2.25× speedup over full attention while maintaining comparable performance.

To tackle the second challenge, we design MotionSiT, along with FlashMo, tailored for motion diffusion, which differs from the standard SiT that interpolates all dimensions together in Euclidean space. Instead, we perform temporal-spatial factorized interpolant with Lie group geodesics 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 to 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 largescale open-source dataset MotionHub V2 [50] and evaluate our method on standard text-to-motion benchmarks, including HumanML3D [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 as 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 2.25× speedup over full attention without sacrificing performance.

We introduce MotionSiT, a diffusion transformer with factorized Lie group interpolant that enables scalable motion diffusion on SO(3) 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 HumanML3D [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-theart 1-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,

CLIP

t

Sparse Transformer

Noised

Latent

Embed

Sparse Transformer

Layer Norm

Noise

Motion

Latent

...

(a) MotionSiT

Layer Norm

Sparse-MHA

Layer Norm

FFN

(b) Sparse Transformer

(c) Frequency-aware Sparsification

QKV

Q

KV

Hi-Fi Heads

Lo-Fi Heads

X

(d) Attention-based Token Selection

Q

K

Attention

Scores

Guide

Token Pruning

Z

个

个

个

个

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 in linear models [47]. However, they have not investigated its role at the attention-head level or its potential for further improving efficiency [57]. These methods have also been applied to 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, semi-structured, and structured schemes. Unstructured sparse attention [75, 55] applies token sparsification without a 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 a pooling operation [81, 45, 27, 84]. However, it often requires custom computational kernels tailored to 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, 9, 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 a frequency-aware sparse attention layer that prunes low-frequency tokens while preserving highfrequency 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 hardware-efficient exact attention [19, 18, 67] without kernel customization, achieving high efficiency without sacrificing performance.

3.2 MotionSiT

MotionSiT is a latent generative model. Given a motion representation M ∈ M L × D 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 X ∈ R T × S , where T = L/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 A.1 , 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.

Temporal-spatial factorized interpolant. The original SiT assumes each input sample is a flat vector and defines a simple linear stochastic interpolant between a noise sample ϵ ∼ N (0 , I ) and the data sample x ∗ via:

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, we introduce a temporal-spatial factorized interpolant (FI) , which applies independent noise schedules along the temporal and spatial dimensions. Specifically, we define:

where α T ( t ) ∈ R T and α S ( t ) ∈ R S are scalar schedules for temporal and spatial dimensions, respectively. The element-wise product ⊙ is broadcasted to modulate x ∗ at each timestep and spatial location individually. This factorization allows flexible noise control across time and joints, reflecting motion's structural characteristics.

Lie group geodesics interpolant. Another limitation of interpolation in vanilla SiT lies in its assumption of Euclidean geometry, as 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 to the motion manifold leads to geometric inconsistency in joint rotations.

Hence, we introduce the Lie group geodesic interpolant (LI) to respect the underlying manifold structure through Lie group geodesics. Given a target motion sample x ∗ ∈ G , where G denotes a Lie group, we first map x ∗ to its tangent space g using the logarithmic map log : G → g . We then interpolate within the tangent space before mapping back to the manifold with the exponential map:

where ϵ is Gaussian noise in the Lie algebra g . This approach ensures that the interpolated sample x t always lies on the manifold and that noise is injected in a way that respects the local geometry of the motion space.

Geometric factorized interpolant. Building on the temporal-spatial factorization introduced earlier, we now incorporate the Lie group geodesic to define the geometric factorized interpolant (GFI) in MotionSiT. Specifically, for motion representation lies on a manifold such as SO(3) , we perform geodesic interpolant on both temporal and spatial dimension:

where x ∗ τ,s denotes the motion state at time τ and spatial location s , and ϵ τ,s 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.

Hi-Fi

Frequency

Heatmap

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 distinct patterns in the attention heatmap, providing strong empirical evidence for designing dynamic token sparsification.

Frequency-aware head partition. Given the number of head N h , we first partition the N h attention heads based on their frequency with an ratio β ∈ [0 , 1] , where the first βN h heads are low-frequency (Lo-Fi) heads H Lo and the remaining (1 -β ) N h heads are high-frequency (Hi-Fi) heads H Hi.

Full attention for Hi-Fi heads. Given a motion latent representation X ∈ R T × S , for each attention head h ∈ H Hi, we define projection matrices W ( h ) q , W ( h ) k , W ( h ) v ∈ R S × d k , where d k is the hidden dimensions for a head. We project X into Q , K , V for each head:

where Q [ X ] ( h ) , K [ X ] ( h ) , V [ X ] ( h ) ∈ R T × d k . We then calculate full attention for each Hi-Fi head:

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-based adaptive token selection. Previous works [100] have successfully incorporated attention-guidance for selecting key motion tokens. However, they are limited to 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 a full attention score in each head A = Q [ X ] ( h ) K [ X ] ( h ) ⊤ ∈ R T × T , the cumulative attention score a x for each token x is calculated by summing the corresponding column, and the normalized cumulative attention score ˜ a x by averaging over the non-zero entries in that column.

̸

Wethen select the minimum number of tokens κ whose cumulative attention scores reach the threshold γ of the total attention mass:

where a sorted ( i ) denotes the i -th highest attention score, obtained by sorting cumulative attention scores a in descending order.

We then select the top κ tokens from X ∈ R T × S according to their normalized cumulative attention scores ˜ a x , and obtain the selected token matrix Z ∈ R κ × S , where κ < T . This allows κ to vary depending on the distribution of attention scores in each attention layer:

Sparse attention calculation. For each attention head h ∈ H Lo, we project X into Q , and Z into K , V for each head:

where Q [ X ] ( h ) ∈ R T × d k , and K [ Z ] ( h ) , V [ Z ] ( h ) ∈ R κ × d k . We then calculate sparse attention for each Lo-Fi head:

Sparse multi-head attention. Hence our frequency-aware sparse multi-head attention can be calculated as:

where W o ∈ R ( N h · d k ) × S projects back to the original latent dimension.

4 Experiments

4.1 Datasets and Evaluation Metrics

Datasets. 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 on standard text-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 a 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.

Evaluation metrics. 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, we calculate Average Inference Time (AIT) for showing efficiency.

4.2 Implementation Details

The encoder and decoder of the VAE consist of 4 layers with a compression rate r = 4 . MotionSiT has a depth of 8, with a frequency ratio β = 0 . 5 and an attention threshold γ = 0 . 95 . Both the VAE and MotionSiT use 4 attention heads with a latent dimension of 512. We employ a frozen text encoder from CLIP ViT-B/32. A constant learning rate of 1 × 10 -4 is used, with a batch size of 256 and the AdamW optimizer. For fair comparison, each model is 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 2.60GHz, with a single NVIDIA H20 96G GPU and 32GB of RAM.

Method

Venue

AIT(s)

↓

R-Precision

↑

FID

↓

MMDist

↓

Diversity

→

MModality

↑

Top-1

Top-2

Top-3

HumanML3D

[31]

Real

-

-

0

.

511

±

.

003

0

.

703

±

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003

0

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797

±

.

002

0

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002

±

.

000

2

.

974

±

.

008

9

.

503

±

.

065

-

MMM[60]

CVPR 2024

0

.

081

0

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504

±

.

003

0

.

696

±

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003

0

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794

±

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002

0

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080

±

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003

2

.

998

±

.

007

9

.

411

±

.

058

1

.

164

±

.

041

MoMask [30]

CVPR 2024

0

.

120

0

.

521

±

.

002

0

.

713

±

.

002

0

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807

±

.

002

0

.

045

±

.

002

2

.

958

±

.

008

-

1

.

241

±

.

040

BAMM[59]

ECCV 2024

0

.

411

0

.

525

±

.

002

0

.

720

±

.

003

0

.

814

±

.

003

0

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055

±

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002

2

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919

±

.

008

9

.

717

±

.

089

1

.

687

±

.

051

MoGenTS [85]

NeurIPS 2024

0

.

181

0

.

529

±

.

003

0

.

719

±

.

002

0

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812

±

.

002

0

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033

±

.

001

2

.

867

±

.

006

9

.

570

±

.

077

-

MotionLCM [17] (1-step)

ECCV 2024

0

.

030

0

.

502

±

.

003

0

.

701

±

.

002

0

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803

±

.

002

0

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467

±

.

012

3

.

022

±

.

009

9

.

631

±

.

066

2

.

172

±

.

082

MotionLCM [17] (2-step)

ECCV 2024

0

.

035

0

.

505

±

.

003

0

.

705

±

.

002

0

.

805

±

.

002

0

.

368

±

.

011

2

.

986

±

.

008

9

.

640

±

.

052

2

.

187

±

.

094

MotionLCM [17] (4-step)

ECCV 2024

0

.

043

0

.

502

±

.

003

0

.

698

±

.

002

0

.

798

±

.

002

0

.

304

±

.

012

3

.

012

±

.

007

9

.

607

±

.

066

2

.

259

±

.

092

EMDM[104]

ECCV 2024

0.050

0

.

498

±

.

007

0

.

684

±

.

006

0

.

786

±

.

006

0

.

112

±

.

019

3

.

110

±

.

027

9.551

±

.

078

1

.

641

±

.

078

Motion Mamba [99]

ECCV 2024

0

.

058

0

.

502

±

.

003

0

.

693

±

.

002

0

.

792

±

.

002

0

.

281

±

.

009

3

.

060

±

.

058

9

.

871

±

.

084

2

.

294

±

.

058

StableMoFusion [38]

MM2024

0

.

499

0

.

553

±

.

003

0

.

748

±

.

002

0

.

841

±

.

002

0

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098

±

.

003

-

9

.

748

±

.

092

1

.

774

±

.

051

Preprint 2024

0

.

031

0

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546

±

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003

0

.

743

±

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002

0

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837

±

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002

0

.

072

±

.

003

2

.

767

±

.

007

9

.

577

±

.

070

1

.

858

±

.

056

Preprint 2024

0

.

038

0

.

551

±

.

003

0

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745

±

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002

0

.

836

±

.

002

0

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049

±

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003

2

.

765

±

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008

9

.

584

±

.

066

1

.

833

±

.

052

Preprint 2024

0

.

050

0

.

553

±

.

003

0

.

746

±

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002

0

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837

±

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002

0

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056

±

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003

2

.

773

±

.

009

9

.

598

±

.

067

1

.

758

±

.

056

Light-T2M [89]

AAAI 2025

0

.

151

0

.

511

±

.

003

0

.

699

±

.

002

0

.

795

±

.

002

0

.

040

±

.

002

3

.

002

±

.

008

-

1

.

670

±

.

061

MotionPCM [41] (1-step)

Preprint 2025

0

.

031

0

.

560

±

.

002

0

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752

±

.

003

0

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844

±

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002

0

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044

±

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003

2

.

711

±

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008

9

.

559

±

.

081

1

.

772

±

.

067

MotionPCM [41] (2-step)

Preprint 2025

0

.

036

0

.

555

±

.

002

0

.

749

±

.

002

0

.

839

±

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002

0

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033

±

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002

2

.

739

±

.

007

9

.

618

±

.

088

1

.

760

±

.

068

MotionPCM [41] (4-step)

Preprint 2025

0

.

045

0

.

559

±

.

003

0

.

752

±

.

003

0

.

842

±

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002

0

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030

±

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002

2

.

716

±

.

008

9

.

575

±

.

082

1

.

714

±

.

062

FlashMo (Ours)

-

0.027

0

.

562

±

.

004

0

.

754

±

.

005

0

.

847

±

.

005

0

.

041

±

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002

2

.

711

±

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006

9

.

614

±

.

056

2

.

812

±

.

046

FlashMo w/ pretrain (Ours)

-

0.027

0.568

±

.

005

0.761

±

.

002

0.851

±

.

003

0.029

±

.

002

2.703

±

.

005

9

.

601

±

.

073

2.851

±

.

069

KIT-ML

[61]

Real

-

-

0

.

424

±

.

005

0

.

649

±

.

006

0

.

779

±

.

006

0

.

031

±

.

004

2

.

788

±

.

012

11

.

08

±

.

097

-

MMM[60]

CVPR 2024

-

0

.

404

±

.

005

0

.

621

±

.

005

0

.

744

±

.

004

0

.

316

±

.

028

2

.

977

±

.

019

10

.

91

±

.

101

1

.

232

±

.

039

MoMask [30]

CVPR 2024

-

0

.

433

±

.

007

0

.

656

±

.

005

0

.

781

±

.

005

0

.

204

±

.

011

2

.

779

±

.

022

-

1

.

131

±

.

043

BAMM[59]

ECCV 2024

-

0

.

438

±

.

009

0

.

661

±

.

009

0

.

788

±

.

005

0

.

183

±

.

013

2

.

723

±

.

026

11

.

01

±

.

094

1

.

609

±

.

065

MoGenTS [85]

NeurIPS 2024

-

0

.

445

±

.

006

0

.

671

±

.

006

0

.

797

±

.

005

0

.

143

±

.

004

2

.

711

±

.

024

10

.

92

±

.

090

-

EMDM[104]

ECCV 2024

-

0

.

443

±

.

006

0

.

660

±

.

006

0

.

780

±

.

005

0

.

261

±

.

014

2

.

874

±

.

015

10

.

96

±

.

093

1

.

343

±

.

089

Motion Mamba [99]

ECCV 2024

-

0

.

419

±

.

006

0

.

645

±

.

005

0

.

765

±

.

006

0

.

307

±

.

041

3

.

021

±

.

025

11.02

±

.

098

1

.

678

±

.

064

StableMoFusion [38]

MM2024

-

0

.

445

±

.

006

0

.

660

±

.

005

0

.

782

±

.

004

0

.

258

±

.

029

-

10

.

94

±

.

077

1

.

362

±

.

062

Light-T2M [89]

AAAI 2025

-

0

.

444

±

.

006

0

.

670

±

.

007

0

.

794

±

.

005

0

.

161

±

.

009

2

.

746

±

.

016

-

1

.

005

±

.

036

MotionPCM [41] (1-step)

Preprint 2025

-

0

.

433

±

.

007

0

.

654

±

.

007

0

.

781

±

.

008

0

.

355

±

.

011

2

.

820

±

.

022

10

.

78

±

.

078

1

.

337

±

.

047

MotionPCM [41] (2-step)

Preprint 2025

-

0

.

437

±

.

005

0

.

664

±

.

005

0

.

787

±

.

006

0

.

294

±

.

011

2

.

844

±

.

018

10

.

83

±

.

094

1

.

254

±

.

050

MotionPCM [41] (4-step)

Preprint 2025

-

0

.

443

±

.

005

0

.

664

±

.

004

0

.

789

±

.

005

0

.

336

±

.

013

2

.

881

±

.

023

10

.

76

±

.

096

1

.

258

±

.

056

FlashMo (Ours)

-

-

0

.

449

±

.

002

0

.

670

±

.

004

0

.

799

±

.

002

0

.

152

±

.

004

2

.

709

±

.

005

10

.

64

±

.

074

3

.

287

±

.

042

FlashMo w/ pretrain (Ours)

-

-

0.453

±

.

001

0.679

±

.

004

0.807

±

.

003

0.132

±

.

005

2.701

±

.

005

10

.

79

±

.

093

3.591

±

.

070

(a) FID vs. AIT(s)

↙

(b) Training Time

↓

(c) Parameters

↓

(d) GFLOPS

↓

4.3 Comparative Study

We compare our method with recent efficient motion diffusion models and VQ-VAE-based 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 1 demonstrate that our method consistently outperforms other approaches across most quantitative performance metrics, achieving a 10% efficiency improvement compared to 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.

(a) R-Precision Top-1

↑

(b) R-Precision Top-2

↑

(c) R-Precision Top-3

↑

(d) FID

↓

(e) MM Dist

↓

(f) MModality

↑

4.4 Ablation Study

Scalability. To showcase our 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, as 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-field diffusion models [54] have explored different interpolant functions α ( t ) and variances σ ( t ) , as discussed in Appendix A.1 . The results in Table 2 show that our geometric factorized interpolant with a linear interpolation function significantly outperforms other interpolants. Furthermore, when leveraging the factorized interpolant, the linear function exhibits a 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-spatial structure of motion, combining it with

GVP breaks the variance preserving property, which is a theoretical guarantee in score-based and velocity-field diffusion training. In contrast, the linear interpolant does not have this issue.

Sparsification parameters. We conducted experiments with different head ratios β and attention thresholds γ , 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 performance. In contrast, our sparsification parameters achieve an excellent balance between efficiency and sparsity, delivering performance comparable to full attention while achieving a 2.25× speedup, enabled by unified training and inference without token granularity mismatch.

Model configurations. We investigate different model configuration including number of heads N h and model depth of MotionSiT, compression ratio r of VAE, compare to our default settings ( N h = 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, a frequency-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 2.25× speedup over full attention without compromising motion quality. Our proposed MotionSiT architecture leverages a 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 state-of-the-art methods in both performance and efficiency. Beyond empirical gains, FlashMo highlights the importance of respecting manifold geometry in diffusion processes, demonstrating that modeling motion trajectories along SO(3) geodesics preserves rotational consistency and yields smoother, physically coherent human motions compared to Euclidean approximations.

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Mingyuan Zhang, Xinying Guo, Liang Pan, Zhongang Cai, Fangzhou Hong, Huirong Li, Lei Yang, and Ziwei Liu. Remodiffuse: Retrieval-augmented motion diffusion model. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 364-373, 2023. 2, 21, 24

Mingyuan Zhang, Huirong Li, Zhongang Cai, Jiawei Ren, Lei Yang, and Ziwei Liu. Finemogen: Fine-grained spatio-temporal motion generation and editing. Advances in Neural Information Processing Systems , 36:13981-13992, 2023. 2, 3, 21, 24

Peiyuan Zhang, Yongqi Chen, Runlong Su, Hangliang Ding, Ion Stoica, Zhenghong Liu, and Hao Zhang. Fast video generation with sliding tile attention. arXiv preprint arXiv:2502.04507 , 2025. 3

Zeyu Zhang, Hang Gao, Akide Liu, Qi Chen, Feng Chen, Yiran Wang, Danning Li, and Hao Tang. Kmm: Key frame mask mamba for extended motion generation. arXiv preprint arXiv:2411.06481 , 2024. 2, 3

Zeyu Zhang, Akide Liu, Qi Chen, Feng Chen, Ian Reid, Richard Hartley, Bohan Zhuang, and Hao Tang. Infinimotion: Mamba boosts memory in transformer for arbitrary long motion generation. arXiv preprint arXiv:2407.10061 , 2024. 3

Zeyu Zhang, Akide Liu, Ian Reid, Richard Hartley, Bohan Zhuang, and Hao Tang. Motion mamba: Efficient and long sequence motion generation. In European Conference on Computer Vision , pages 265-282. Springer, 2024. 2, 3, 7, 21, 24

Zeyu Zhang, Yiran Wang, Wei Mao, Danning Li, Rui Zhao, Biao Wu, Zirui Song, Bohan Zhuang, Ian Reid, and Richard Hartley. Motion anything: Any to motion generation. arXiv preprint arXiv:2503.06955 , 2025. 2, 5

Zeyu Zhang, Yiran Wang, Biao Wu, Shuo Chen, Zhiyuan Zhang, Shiya Huang, Wenbo Zhang, Meng Fang, Ling Chen, and Yang Zhao. Motion avatar: Generate human and animal avatars with arbitrary motion. arXiv preprint arXiv:2405.11286 , 2024. 2

Xuanlei Zhao, Xiaolong Jin, Kai Wang, and Yang You. Real-time video generation with pyramid attention broadcast. arXiv preprint arXiv:2408.12588 , 2024. 3

Qihui Zhou, Peiqi Yin, Pengfei Zuo, and James Cheng. Progressive sparse attention: Algorithm and system co-design for efficient attention in llm serving. arXiv preprint arXiv:2503.00392 , 2025. 3

Wenyang Zhou, Zhiyang Dou, Zeyu Cao, Zhouyingcheng Liao, Jingbo Wang, Wenjia Wang, Yuan Liu, Taku Komura, Wenping Wang, and Lingjie Liu. Emdm: Efficient motion diffusion model for fast and high-quality motion generation. In European Conference on Computer Vision , pages 18-38. Springer, 2024. 2, 3, 7, 21, 24

Qianchao Zhu, Jiangfei Duan, Chang Chen, Siran Liu, Xiuhong Li, Guanyu Feng, Xin Lv, Huanqi Cao, Xiao Chuanfu, Xingcheng Zhang, et al. Sampleattention: Near-lossless acceleration of long context llm inference with adaptive structured sparse attention. arXiv preprint arXiv:2406.15486 , 2024. 3

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 of time-indexed stochastic processes that map a noise sample x 0 ∼ p 0 to an intermediate point x ( t ) over t ∈ [0 , 1] . These interpolants are defined without requiring a target sample x 1 (as in standard DDPMs), and instead use structured noise perturbation with learned dynamics to generate samples.

The interpolant is defined as:

where α ( t ) and σ ( t ) are time-dependent scalar functions, x 0 is a noise sample (analogous to initial state in forward diffusion). It holds that x (0) = x 0 , and x (1) ideally follows p 1 , the data distribution.

These interpolants allow generation and learning without explicitly simulating a forward diffusion trajectory from x 1 to x 0 as in traditional diffusion models.

Forward SDE. The forward generative process in diffusion models is typically described using a stochastic differential equation of the form:

where f ( x , t ) is a drift function, g ( t ) is a diffusion coefficient, w t 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:

where ∇ x log p t ( x ) is the score function , which denotes the gradient of the log-density at time t , d ¯ w t is a 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 is given by the probability flow ODE:

Unlike the reverse SDE, this ODE yields a deterministic mapping from x 0 to x 1 . Critically, both the reverse-time SDE and the probability flow ODE share the same marginal distribution p t ( x ) for each t .

This connection allows one to model generation either stochastically (via sampling the reverse SDE) or deterministically (via integrating the ODE). In 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 the score function ∇ x log p t ( x ) , SiT models the trajectory of the interpolant x ( t ) by learning its velocity field :

Given the analytic form of the interpolant:

its time derivative is:

Since x 0 and ϵ are both known (sampled during training), this velocity is analytically computable.

The SiT model learns a velocity estimator v θ ( x ( t ) , t ) by minimizing the expected squared error between the predicted and true velocity:

This learning objective removes the need for score estimation or denoising objectives and allows SiT to scale better.

Interpolants design. The choice of interpolant functions α ( t ) , the deterministic scaling of the input noise x 0 and σ ( t ) , the time-dependent standard deviation controlling the magnitude of the stochastic perturbation directly determines the geometry of the stochastic path x ( t ) = α ( t ) x 0 + σ ( t ) ϵ . This, in turn, influences training dynamics, sample quality, and generalization. Below, we describe two common interpolant designs that capture different trade-offs

The linear interpolant is defined as:

This design induces a linear interpolation in the input x 0 and a square-root scaling of the noise. At t = 0 , we have x (0) = x 0 ; at t = 1 , α (1) = 0 and σ (1) = 1 , hence x (1) = ϵ , a pure noise sample.

This interpolant is simple and intuitive, but its marginal distribution p t ( x ) varies in both mean and variance across time. Specifically:

Thus, the total variance is time-varying.

To address the variance inconsistency, the GVP interpolant is constructed such that the total variance remains constant over time:

A canoncial choice under this constraint is:

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 x 0 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 non-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:

which is a compact, connected, non-commutative Lie group of dimension 3. Each element of SO(3) represents a proper rotation in R 3 , and the group operation is matrix multiplication. The non-Euclidean nature of SO(3) implies that standard linear operations, such as averaging two rotation matrices or interpolating between them, may lead to results that no longer lie on the manifold.

This design ensures that:

The Special Euclidean Group SE(3) . For full rigid body transformations, including both rotation and translation, the appropriate Lie group is SE(3) :

which is a 6-dimensional, non-compact, non-commutative Lie group. The group operation is 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 G is a Lie algebra g , which serves as the tangent space at the identity element and provides a local, linear coordinate system for the manifold. For SO(3) , the Lie algebra so (3) consists of all 3D skew-symmetric matrices:

A standard isomorphism between R 3 and so (3) is provided by the hat operator :

which maps a 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 → G and its inverse, the logarithmic map log : G → 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:

This constructs a rotation matrix corresponding to a rotation of angle θ around axis ω/ ∥ ω ∥ . The logarithmic map inverts this operation, computing the minimal-axis rotation vector ω corresponding to a given rotation matrix R :

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 R 1 , R 2 ∈ SO(3) , a geodesic interpolation can be defined via:

which traces the shortest path on the manifold between R 1 and R 2 . More generally, any operation of the form:

defines a distribution on SO(3) by sampling from a Gaussian in the tangent space R 3 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 se (3) is a 6-dimensional space encoding translational and rotational velocities. Elements of se (3) are typically expressed using twist coordinates ( v, ω ) ∈ R 6 , and the exponential or logarithmic maps generalize accordingly. The Baker-Campbell-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 a consistent, geometry-aware manner, and is indispensable in domains such as robotics, graphics, and motion modeling.

B User Study

This study conducts a comprehensive user evaluation of our method compared with MotionLCM [17] and MoGenTS [85]. We assess the real-world applicability of motion sequences generated by Motion Anything and baseline models using a Google Forms survey completed by 50 participants. As shown in Figure 7, the user interface presents 3-4 motion clips (Videos 1-3/4) generated by the same model, followed by a comparative set (Videos A-C) from different models. Participants evaluate each animation based on motion accuracy and overall user experience, using a 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 it produces high-quality motion with minimal jitter, sliding, or unrealistic artifacts.

For motion diversity, we received a rating of 2.86 , with 90% of participants indicating that our method generates complex and varied motion sequences.

In terms of text-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, 92% of participants preferred our method over competing approaches.

C Qualitative Results

To qualitatively evaluate our performance in text-to-motion generation, we 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 HumanML3D [31] test set. As shown in Figure 8 and video demos, our method generates motions with superior quality, greater diversity, and better alignment between text and motion compared to the previous state-of-the-art methods.

D Full Comparison Tables

To comprehensively evaluate our method on text-to-motion generation, we report full comparisons with prior approaches in Table 5 and 6. Our method consistently achieves state-of-the-art 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 a 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, our approach lowers the barrier to adoption in resource-constrained settings and paves the way for real-time, interactive, and embodied AI systems.

F Limitations and Future Work

Currently, our design has not yet explored other interpolant functions, and we still rely on learning latent diffusion models on the tangent space. In future work, we will investigate how different interpolant forms benefit temporal-spatial 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.

Method

Venue

AIT(s)

↓

R-Precision

↑

FID

↓

MMDist

↓

Diversity

→

MModality

↑

Top-1

Top-2

Top-3

Real

-

-

0

.

511

±

.

003

0

.

703

±

.

003

0

.

797

±

.

002

0

.

002

±

.

000

2

.

974

±

.

008

9

.

503

±

.

065

-

TM2T [32]

ECCV 2022

0

.

760

0

.

424

±

.

003

0

.

618

±

.

003

0

.

729

±

.

002

1

.

501

±

.

017

3

.

467

±

.

011

8

.

589

±

.

076

2

.

424

±

.

093

T2M-GPT [92]

CVPR 2023

0

.

380

0

.

492

±

.

003

0

.

679

±

.

002

0

.

775

±

.

002

0

.

141

±

.

005

3

.

121

±

.

009

9

.

722

±

.

082

1

.

831

±

.

048

CoMo [39]

ECCV 2024

0

.

620

0

.

502

±

.

002

0

.

692

±

.

007

0

.

790

±

.

002

0

.

262

±

.

004

3

.

032

±

.

015

9

.

936

±

.

066

1

.

013

±

.

046

MMM[60]

CVPR 2024

0

.

081

0

.

504

±

.

003

0

.

696

±

.

003

0

.

794

±

.

002

0

.

080

±

.

003

2

.

998

±

.

007

9

.

411

±

.

058

1

.

164

±

.

041

MoMask [30]

CVPR 2024

0

.

120

0

.

521

±

.

002

0

.

713

±

.

002

0

.

807

±

.

002

0

.

045

±

.

002

2

.

958

±

.

008

-

1

.

241

±

.

040

BAMM[59]

ECCV 2024

0

.

411

0

.

525

±

.

002

0

.

720

±

.

003

0

.

814

±

.

003

0

.

055

±

.

002

2

.

919

±

.

008

9

.

717

±

.

089

1

.

687

±

.

051

MoGenTS [85]

NeurIPS 2024

0

.

181

0

.

529

±

.

003

0

.

719

±

.

002

0

.

812

±

.

002

0

.

033

±

.

001

2

.

867

±

.

006

9

.

570

±

.

077

-

MDM[70]

ICLR 2023

24.74

0

.

320

±

.

005

0

.

498

±

.

004

0

.

611

±

.

007

0

.

544

±

.

044

5

.

566

±

.

027

9

.

559

±

.

086

2

.

799

±

.

072

MotionDiffuse [93]

TPAMI 2024

14

.

74

0

.

491

±

.

001

0

.

681

±

.

001

0

.

782

±

.

001

0

.

630

±

.

001

3

.

113

±

.

001

9

.

410

±

.

049

1

.

553

±

.

042

MLD [7]

CVPR 2023

0.217

0

.

481

±

.

003

0

.

673

±

.

003

0

.

772

±

.

002

0

.

473

±

.

013

3

.

196

±

.

010

9

.

724

±

.

082

2

.

413

±

.

079

ReMoDiffuse [94]

ICCV 2023

0

.

624

0

.

510

±

.

005

0

.

698

±

.

006

0

.

795

±

.

004

0

.

103

±

.

004

2

.

974

±

.

016

9

.

018

±

.

075

1

.

795

±

.

043

M2DM [44]

ICCV 2023

-

0

.

497

±

.

003

0

.

682

±

.

002

0

.

763

±

.

003

0

.

352

±

.

005

3

.

134

±

.

010

9

.

926

±

.

073

3.587

±

.

072

Fg-T2M [73]

ICCV 2023

-

0

.

492

±

.

002

0

.

683

±

.

003

0

.

783

±

.

002

0

.

243

±

.

019

3

.

109

±

.

007

9

.

278

±

.

072

1

.

614

±

.

049

FineMoGen [95]

NeurIPS 2023

-

0

.

504

±

.

002

0

.

690

±

.

002

0

.

784

±

.

002

0

.

151

±

.

008

2

.

998

±

.

008

9

.

263

±

.

094

2

.

696

±

.

079

GraphMotion [42] (50-step)

NeurIPS 2023

0

.

776

0

.

496

±

.

003

0

.

686

±

.

003

0

.

778

±

.

002

0

.

118

±

.

008

3

.

143

±

.

009

9

.

796

±

.

069

2

.

603

±

.

095

GraphMotion [42] (150-step)

NeurIPS 2023

2

.

552

0

.

504

±

.

003

0

.

699

±

.

002

0

.

785

±

.

002

0

.

116

±

.

007

3

.

070

±

.

008

9

.

692

±

.

067

2

.

766

±

.

096

B2A-HDM [80]

AAAI 2024

-

0

.

511

±

.

002

0

.

699

±

.

002

0

.

791

±

.

002

0

.

084

±

.

004

3

.

020

±

.

010

9.526

±

.

080

1

.

914

±

.

078

M2D2M [12]

ECCV 2024

-

-

-

0

.

799

±

.

002

0

.

087

±

.

004

3

.

018

±

.

008

9

.

672

±

.

086

2

.

115

±

.

079

MotionLCM [17] (1-step)

ECCV 2024

0

.

030

0

.

502

±

.

003

0

.

701

±

.

002

0

.

803

±

.

002

0

.

467

±

.

012

3

.

022

±

.

009

9

.

631

±

.

066

2

.

172

±

.

082

MotionLCM [17] (2-step)

ECCV 2024

0

.

035

0

.

505

±

.

003

0

.

705

±

.

002

0

.

805

±

.

002

0

.

368

±

.

011

2

.

986

±

.

008

9

.

640

±

.

052

2

.

187

±

.

094

MotionLCM [17] (4-step)

ECCV 2024

0

.

043

0

.

502

±

.

003

0

.

698

±

.

002

0

.

798

±

.

002

0

.

304

±

.

012

3

.

012

±

.

007

9

.

607

±

.

066

2

.

259

±

.

092

EMDM[104]

ECCV 2024

0.050

0

.

498

±

.

007

0

.

684

±

.

006

0

.

786

±

.

006

0

.

112

±

.

019

3

.

110

±

.

027

9

.

551

±

.

078

1

.

641

±

.

078

Motion Mamba [99]

ECCV 2024

0

.

058

0

.

502

±

.

003

0

.

693

±

.

002

0

.

792

±

.

002

0

.

281

±

.

009

3

.

060

±

.

058

9

.

871

±

.

084

2

.

294

±

.

058

StableMoFusion [38]

MM2024

0

.

499

0

.

553

±

.

003

0

.

748

±

.

002

0

.

841

±

.

002

0

.

098

±

.

003

-

9

.

748

±

.

092

1

.

774

±

.

051

Preprint 2024

0

.

031

0

.

546

±

.

003

0

.

743

±

.

002

0

.

837

±

.

002

0

.

072

±

.

003

2

.

767

±

.

007

9

.

577

±

.

070

1

.

858

±

.

056

Preprint 2024

0

.

038

0

.

551

±

.

003

0

.

745

±

.

002

0

.

836

±

.

002

0

.

049

±

.

003

2

.

765

±

.

008

9

.

584

±

.

066

1

.

833

±

.

052

Preprint 2024

0

.

050

0

.

553

±

.

003

0

.

746

±

.

002

0

.

837

±

.

002

0

.

056

±

.

003

2

.

773

±

.

009

9

.

598

±

.

067

1

.

758

±

.

056

MMDM-t [8]

Preprint 2024

-

0

.

464

±

.

006

0

.

654

±

.

007

0

.

754

±

.

005

0

.

319

±

.

026

3

.

288

±

.

023

9

.

299

±

.

064

2

.

741

±

.

112

MMDM-b [8]

Preprint 2024

-

0

.

435

±

.

006

0

.

627

±

.

006

0

.

733

±

.

007

0

.

285

±

.

032

3

.

363

±

.

029

9

.

398

±

.

088

2

.

701

±

.

083

FTMoMamba [47]

Preprint 2024

-

0

.

489

±

.

003

0

.

680

±

.

002

0

.

777

±

.

002

0

.

181

±

.

009

3

.

151

±

.

009

9

.

789

±

.

085

2

.

277

±

.

099

Light-T2M [89]

AAAI 2025

0

.

151

0

.

511

±

.

003

0

.

699

±

.

002

0

.

795

±

.

002

0

.

040

±

.

002

3

.

002

±

.

008

-

1

.

670

±

.

061

Free-MDM [6]

Preprint 2025

0

.

045

0

.

466

±

.

008

0

.

657

±

.

007

0

.

757

±

.

005

0

.

256

±

.

045

-

9

.

666

±

.

080

-

Free-StableMoFusion [6]

Preprint 2025

0

.

036

0

.

520

±

.

013

0

.

707

±

.

003

0

.

803

±

.

006

0

.

051

±

.

002

-

9.480

±

.

005

-

MotionPCM [41] (1-step)

Preprint 2025

0

.

031

0

.

560

±

.

002

0

.

752

±

.

003

0

.

844

±

.

002

0

.

044

±

.

003

2

.

711

±

.

008

9

.

559

±

.

081

1

.

772

±

.

067

MotionPCM [41] (2-step)

Preprint 2025

0

.

036

0

.

555

±

.

002

0

.

749

±

.

002

0

.

839

±

.

002

0

.

033

±

.

002

2

.

739

±

.

007

9

.

618

±

.

088

1

.

760

±

.

068

MotionPCM [41] (4-step)

Preprint 2025

0

.

045

0

.

559

±

.

003

0

.

752

±

.

003

0

.

842

±

.

002

0

.

030

±

.

002

2

.

716

±

.

008

9

.

575

±

.

082

1

.

714

±

.

062

Fg-T2M++ [74]

IJCV 2025

-

0

.

513

±

.

002

0

.

702

±

.

002

0

.

801

±

.

003

0

.

089

±

.

004

2

.

925

±

.

007

9

.

223

±

.

114

2

.

625

±

.

084

BioMoDiffuse [43]

Preprint 2025

-

0

.

547

±

.

003

0

.

743

±

.

002

0

.

835

±

.

002

0

.

071

±

.

003

2

.

784

±

.

008

9

.

567

±

.

086

1

.

919

±

.

063

HiSTF Mamba [90] (10-step)

Preprint 2025

0

.

690

0

.

488

±

.

005

0

.

685

±

.

004

0

.

784

±

.

005

0

.

189

±

.

018

3

.

101

±

.

022

9

.

712

±

.

090

2

.

529

±

.

044

HiSTF Mamba [90] (15-step)

Preprint 2025

-

0

.

504

±

.

005

0

.

699

±

.

005

0

.

798

±

.

005

0

.

249

±

.

023

3

.

053

±

.

022

9

.

383

±

.

091

2

.

276

±

.

036

ACMo [76]

Preprint 2025

-

0

.

493

±

.

002

0

.

698

±

.

003

0

.

795

±

.

002

0

.

102

±

.

003

2

.

973

±

.

006

9

.

749

±

.

082

2

.

614

±

.

100

FlashMo (Ours)

-

0.027

0

.

562

±

.

004

0

.

754

±

.

005

0

.

847

±

.

005

0

.

041

±

.

002

2

.

711

±

.

006

9

.

614

±

.

056

2

.

812

±

.

046

FlashMo w/ pretrain (Ours)

-

0.027

0.568

±

.

005

0.761

±

.

002

0.851

±

.

003

0.029

±

.

002

2.703

±

.

005

9

.

601

±

.

073

2

.

851

±

.

069

Section 1

Section 1.2

There ere two mejor questions for Section 1.

The firetquestion requeste youto overall qualfy the motions provided and forthe second

questicn please choose the best motion from a set of motions.

Seclion 1.1

Section1.2.1

The sim appears to walk forward bend slightly graboing an object with thelr let

hand.

Sectlonl.1

section1.2.2

A person is spinning wilh is arms spread out and then he falls over.

Section1.1.2

C

He starts to dance a lot.

section1.2.3

Sectlon11.

Question 1

Question 4

Kjeoyoods *suogeyousB uogow popinoud eug go Aqgenb jesono oug ssosse eseold

Which video you believe that has the best motion quality?

determine whether they exhibit noticeable jiter, sliding, or unresFstio mcvement,

and select the statement that best reflecls your evaluation.

O

The visuallzation is acceptable, but there are several aspects that need

Improvement.

The visualization is strong, highlighting the potentialimpect and promising future for

applications.

Question 5

Question2

Which video you believe that has the highest diversity?

Please evaluaie the average diversity of the moticn generations provided and

seleot the statement that best refleots your assessment

O

O

O The motions exnbit moderale dversity, though cerein elements are sil predictable.

O

The motions ere highly divsrae, shcwcasing a wide range of movsment patterns end

creative variations.

Question 6

Question3

Which motion do you believe best aligns with or reflects the text: *Person walks

Plesse evaluale the overall degree of match belween the texls and their

forwards straight while stumbling"?

assessment:

O

O There Is Ittle to no allorment between the texts and motlons.

O

O The texis end motions elign feinly well,though some details inconsistencies emain.

O The texis eand motions are wel-aligned, accuretely reflecting each other.

○：

MotionLCM

MoGenTS

FlashMo

The man appears to

walk forward bend

slightly grabbing an

object with their left

hand.

Text

A person is spinning

with is arms spread out

and then he falls over.

The man starts to

dance a lot.

A person walks

forwards straight while

stumbling.

A person does karate

by striking with their

hands and kicking

their legs.

萧

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n### 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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n- 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).\n\nSTRENGTHS AND WEAKNESSES:\n- **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\n\nQUESTIONS:\n**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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-23"}

comments: 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.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The frequency-aware sparse attention mechanism is well-motivated, practical, and integrates seamlessly with hardware-optimized exact attention.
2. MotionSiT decouples temporal and spatial dimensions and applies lie group geodesic interpolation, which enhances the structural integrity of motion sequences.
3. The proposed method shows better performance and becomes more efficient.
Weakness:
1. In Figure 2 (d), the Guide Token Pruning should be detailed in the figure for easier understanding.
2. 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?

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.
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\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.\n\nQUESTIONS:\nThe 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}