TriC-Motion: Tri-Domain Causal Modeling Grounded Text-to-Motion Generation
Authors: Yiyang Cao
Yunze Deng
Ziyu Lin
Bin Feng
Xinggang Wang
Wenyu Liu
DanDan Zheng
Jingdong Chen
Venue: ICLR 2026 Poster (2026)
Keywords: Text-to-Motion Generation
Spatial-temporal-frequency Modeling
Causal Learning
Primary area: generative models

Abstract: Text-to-motion generation, a rapidly evolving field in computer vision, aims to produce realistic and text-aligned motion sequences. Current methods primarily focus on spatial-temporal modeling or independent frequency domain analysis, lacking a unified framework for joint optimization across spatial, temporal, and frequency domains. This limitation hinders the model's ability to leverage information from all domains simultaneously, leading to suboptimal generation quality. Additionally, in motion generation frameworks, motion-irrelevant cues caused by noise are often entangled with features that contribute positively to generation, thereby leading to motion distortion. To address these issues, we propose Tri-Domain Causal Text-to-Motion Generation (TriC-Motion), a novel diffusion-based framework integrating spatial-temporal-frequency-domain modeling with causal intervention. TriC-Motion includes three core modeling modules for domain-specific modeling, namely Temporal Motion Encoding, Spatial Topology Modeling, and Hybrid Frequency Analysis. After comprehensive modeling, a Score-guided Tri-domain Fusion module integrates valuable information from the triple domains, simultaneously ensuring temporal consistency, spatial topology, motion trends, and dynamics. Moreover, the Causality-based Counterfactual Motion Disentangler is meticulously designed to expose motion-irrelevant cues to eliminate noise, disentangling the real modeling contributions of each domain for superior generation. Extensive experimental results validate that TriC-Motion achieves superior performance compared to state-of-the-art methods, attaining an outstanding R@1 of 0.612 on the HumanML3D dataset. These results demonstrate its capability to generate high-fidelity, coherent, diverse, and text-aligned motion sequences. Code is available at: \url{https://caoyiyang1105.github.io/TriC-Motion/}.

Published as a conference paper at ICLR 2026
TRIC-MOTION: TRI-DOMAIN CAUSAL MODELING GROUNDED TEXT-TO-MOTION GENERATION
Yiyang Cao Yunze Deng" 1. Ziyu Lin", Bin Feng't Xinggang Wang', Wenyu Liu',
DanDan Zheng Jingdong Chen 315
School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
2 Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA 3 Ant Group, Beijing. China
fyiyangeao, yunzedeng, fengbin (aassn)ehuuteee.. (yuandan. zdd, Jingdongchhen)aarte gant com
ABSTRACT
Text-to-motion generation, a rapidly evolving field computer vision, aims produce realistic and text-aligned motion sequences. Current methods primar- ily focus on spatial-temporal modeling or independent frequency domain analy- sis, lacking unified framework for joint optimization across spatial, temporal, and frequency domains. This limitation hinders the model's ability to leverage information from all domains simultaneously, leading to suboptimal generation quality. Additionally, in motion generation frameworks, motion-irrelevan cues caused by noise often entangled with features that contribute positively generation, thereby leading to motion distortion. To address these issues, we propose Tri-Domain Causal Text-to-Motion Generation (TriC-Motion), novel diffusion-based framework integrating eling with causal intervention. TriC-Motion includes three core modeling mod- ules for domain-specin Topology Modeling, and Hybrid Frequency Analysis. After comprehensive mod- eling. a Score-guided guided Tri-domain Fusion module integrates valuable information from the triple domains, simultaneously ensuring temporal consistency, spatial topology, motion trends, and dynamics. Morcover, the Causality-basee Counter- factual Motion Disentangler is meticulously designed to expose motion-irrelevan cues to eliminate noise, disentangling the real modeling contributions of each do- main for superior generation. Extensive experimental results validate that TriC- Motion achieves superior performance compared state-of-the-art methods, at- taining an outstanding R @ of 0.612 on the HumanML3D dataset. These results demonstrate capability to generate high-fidelity, coherent, diverse, and text- aligned motion sequences. Code is available at: https: //aayyyyagggllo/// gi rithub... wwt.IriC-oooiiiiioio..
mod-
modeling, namely Temporal Motion Encoding, Spatial
INTROUCTION
Text-driven human motion generation is rapidly emerging as a focal research area in computer vision, with broad potential impact across the film and game industry (Shuto et al., 2025), hu- man-computer interaction (Wang al., 2025; Sui et al., 2025), and embodied intelligence in robotics (Long et al., 2025). This task involves interpreting textual descriptions to produce smooth, physically plausible, and semantically coherent joint coordinate sequences, such as natural poses for walking, running, jumping (Xue al., 2025).
"Equal contribution.
"The first corresponding author.
"The second corresponding authors.
This work was supported by National Natural Science Foundation of China (No.62376102) and Ant Group

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Published as a conference paper at ICLR 2026
profonee loudood.
diw prewed
AJTB
We spaipilll modiling
Wi Proqunccy modillag
wiss cnnaaattvattion
REDO
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(a)
(b)
Figure 1: (a) Visual comparison of motion generated before and after spatial modeling/frequuncc modeling/causal intervention; (b) Quantitative comparison of different methods' performance on HumanML3D.
Recent text-to-motion methods can be broadly divided into diffusion-based (Tevet et al., 2022; Zhang et al., 2023b; 2024a; Zhou al., 2024) and auto-regressive approaches (Zhang et al., 2023a; Pinyoanuntapong et al., 2024; Guo et al., 2024), primarily focusing temporal modeling to depict dynamic evolution and ensure sequence consistency. Recent works (Yuan al., 2024; Zhang al., 2024b) extend to spatial-temporal joint modeling. further enhancing motion realism and topologi- cal consistency while improving motion quality, Typically, Spatio- Temporal Graph Diffusion (Liu et al., 2023) encodes local joint topologies via graph convolutions and characterizes dynamic evo- lution through 1-D temporal convolutions, while HiSTF Mamba (Zhan et al., 2025) captures short- and long-range spatial-temporal clues using bidirectional Mamba. However, generating motions with high or complex dynamics still remains challenging.
Given the successful application of spectral analysis in various fields (Hyun et al., 2023; Chen al., 2024a; Li et al., 2024b; Kim et al., 2024), some motion generation methods (Xu & Chen, 2024; Li al., 2024a; Wan al., 2023) address the aforementioned issues by independently analyzing low-frequency and high-frequency signals. For human motion, low-frequency components capture global evolution for smoothness (i.e., coarse motion trends), while high-frequency components de- pict subtle joint dynamics for richness (i.e., fine motion details) (Li et al., 2024a). Jointly attending these complementary bands promotes generations that are both coherent and richly detailed.
Despite significant progress, unified motion generation framework integrating spatial, temporal, and frequency domains remains unexplored. Fig. 1(a)l highlights that missing spatial modeling leads to unrealistic joint topology, while Fig. (a))l demonstrates that high- and low-frequency modeling ensures overall trends and fine dynamic details. Therefore, we argue that integrating all three do- mains into one framework fully utilizes complementary information, enabling comprehensive mo- tion representations for high-quality generation. This approach has proven effective in various tasks such target detection (Duan et al., 2024; Huang et al., 2025; Duan et al., 2025), classification (Liu et al., 2021), 1), and image decoding (Cao et al., 2024), highlighting its potential for motion generation.
Another potential issue is that motion-irrelevant cues caused by noise during motion generation modeling are often entangled within the features, thereby leading to ineffective modeling and de- grading fidelity. In a naive multi-domain architecture, this issue even worsens as noise accumulates across domains. Inspired by causal intervention (Pearl et al., 2016; Yang et al., 2023; Xiong et al., 2024), we identify this problem as the model's inability to distinguish beneficial factual features from motion-in motion-irrelevant irrelevant founterfactual within our tri-domain architecture (Sec. 2) extract beneficial causal contributions, disentangle and eliminate motion-irrelevan cues, thereby focusing on valuable complementary cross-domain infor- mation. As shown in Fig. ((a)III, such intervention effectively mitigates quality degradation caused by multi-domain noise accumulation, ultimately enhancing motion fidelity.
To address this, we employ structural causal model
Therefore, we propose Tri-Domain Causal Text-to-Motion Generation (TriC-Motion), a novel framework integrating causal intervention with tri-domain modeling for motion generation. Built on MDM (Tevet et al., 2022), it employs multiple TriC-Motion Denoiser Blocks, each including three modules: Temporal Motion Encoding (TME), Spatial Topology Modeling (STM), and Hybrid Frequency Analysis (HFA) modules. Subsequently, the Score-guided Tri-domain Fusion (S-Fus) module efficiently integrates tri-domain information, ensuring temporal consistency, spatial topol-
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ogy, accurate overall motion trends, and fine fine-grained dynamics. Additionally, during training, the Causality-barn based Counterfactual Motion Disentangler (CCMD) is applied in each block to disentan- gle inreionnrreelevant cues, guiding tri-domain modeling toward key motion information. To the best of our knowledge, TriC-Motion is the first method to introduce causal intervention into motion gen- eration. Compared previous methods, it captures essential tri-domain features, enabling higher- quality motion generation. As shown in Fig. 1(b), TriC-Motion achieves 0.612 of RI-Precision (R @ I) and sets new state-of-the-ar performance across most metrics on HumanML3D (Guo al., 2022), with visualizations showing improved fidelity, diversity, and semantic consistency.
Our main contributions can be summarized as follows: (1) We propose a novel motion genera- tion framework that unifies spatial-temporal frrquurcl- modeling within a diffusion-basse denois- ing architecture. The collaborative integration of information from the three domains enables the model to capture temporal dynamics, spatial topology, and multi-granul multi-granularity frequency character- istics, thereby improving generation quality and fidelity.
(2) We pioneer the introduction of causal intervention into motion generation, designing an innovative Causality-based Counterfactual Mo- tion Disentangler module. This module effectively removes redundant information and noise in tri-domain modeling, stabilizes the denoising process, and guides each domain to focus key mo- tion features for better generation. (3) We conduct comprehensive evaluations on HumanML3D and SnapMoGen, achieving state-of-the-art results most metries. Extensive ablation studies further confirm the effectiveness of each domain branch and the causal intervention design.
2
RELATED WORK
2.1 SPATIAL- TETIIAL-TEMPOAL-FREEOEE
MODELING IN MOTION GENERATION
Temporal modeling (Zhang et al., 2024a; Tevet et al., 2022; Zhang et al., 2024b; 2023a; Guo et al., 2024; Pinyoanuntapong et al., 2024) has been the dominant paradigm in text-to-motion generation. Spatial priors further enhance plausibility, such as graph-base based spatio-temporal convolutions (Liu al., 2023) and joint-token attention (Yuan al., 2024). Frequency modeling complements these by leveraging low- and high-frequency components for global trends and fine details (Wan et al., 2023; Li al., 2024a). However, unified framework that jointly optimizes temporal, spatial, and frequency cues remains underexplored, hindering coherent, physically plausible, and high-quality motion generation.
2.2 CAUSALITY IN COMPUTER VISION
Causality has become increasingly powerful tool in computer vision: (Yang et al., 2021) mitigate confounding in vision-language alignment via causal attention; (Chen et al., 2023a) address do- main shift through meta-causal learning; and, for gait recognition, counterfactual interventions are adopted to suppress non-identity factors(e.g., appearance, (oad-carrying)(Xionn et al., 2024; Dou al., 2023). Yet generative modeling, especially text-to-motion, remains underexplored. TriC- Motion adopts an SCM SCM-inspired view of tri-domain features as mixtures of intrinsic signals and confounders and performs interventions during diffusion to preserve causal contributions and re- move motion-irrelevnn information.
METHOD
THC Marttan Dentier awng EMEE STM VARL
3.1 PRELIMINARY
F
X
Causal Learning. Causal inference and causal intervention are the core concepts of causal learning, with the former iden- tifying cause-and-effect relationships and the latter disrupting original causal connections (Pearl et al., 2016). Inspired by Structured Causal Models (Zhang, 1994), we present Fig. 2 to better illustrate how TriC-Motion integrates causal learn- ing into motion generation. Specifically, the j-th layer of TriC-Motion Denoiser Block extracts domain-specific fea- tures (i E (temp, spa, freq)) through carefully designed
Interveatio Mark
Direct causal impact X do() operation Indirect statistical relntionship
Figure 2: Structured Casual Model in TriC-Motion.
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modeling, which are typically entangled results of intrinsic motion characteristics Ej and con- founders Cj (e.g., motion-irrelevan cues) that adversely affect generation. These features are then used to generate the motion sequence To. Ideally, Fj should consist solely of Ej. i.e, bj F + Zo, where + denotes direct causal impact. However, in practice, the motion fea- ture modeling process invariably includes motion- iirelevante cues, which couple with Ej. leading to EC). - F) zu- Since directly removing Cj is challenging, we design the CCMD module to perform causal intervention do(-). isolating the causal impact of Cj on FÝ. This operation ensures that generated motions influenced primarily by the intrinsic motion characteristics rather than confounders, thereby enhancing the overall quality.
3.2 PIPELINE
The overview of TriC-Motion is illustrated in Fig. 3(b). Our framework can be divided into two key parts: TriC-Motion Denoiser Blocks and Causal Intervention Blocks. The former are dedicated to capturing the tri-domain characteristics of motion sequences, while the latter disentangle and eliminate motion-irrelevant cues from each domain.
TriC-Motion builds upon MDM (Tevet et al., 2022)'s diffusion and sampling processes, and its sampling process and network architecture are illustrated in Fig. 3. Following (Yuan et al., 2024), the motion sequence preprocessed from ID temporal structure to facilitate subsequent modeling. Then, the sequence is downsampled temporally by a factor of and projected into the feature dimension via a linear layer, forming X R R& where N, M, and D denote downsampled frames, number of joints, and feature dimensionality, respectively. The diffusion timestep is omitted for simplicity. To incorporate spatial-temporal position information, the original ID sinusoidal positional encoding is replaced by its 2D variant (Wang & Liu, 2021), which is then added to the motion sequence. Besides, the pretrained text encoder DistilBERT (Sanh et al., 2019) encodes the provided prompt, generating sentence-level features CLS and word-level features т. The diffusion timestep t is projected to the feature dimension via a ferd-forwwrd network and concatenated with CLS and motion features, yielding X E (N++2)xMMX
2D spatial-temporal structure
The TriC-Motion Denoiser stacks J layers of identical blocks. At the j-th layer and timestep i, mo- tion features X, are processed in parallel across temporal, spatial, and frequency domains by three modules (TME, STM, HFA), yielding domain-specific features Fj. where {temp, spa, freq} represents the domain index. Following this, the S-Fus module integrates Fj by leveraging seman- tic information and motion-specific properties from all domains, producing the fused feature Yj- Finally, semantic information is injected via cross-attention in the Textual Information Injection (TIJ) module, where word-level features 7 act as keys and values, and fused motion features serve as queries. The complete process of the tri-domain modeling over J blocks can be formulated as: X = (X, T) = CrossAttention X,,T,T)
(1)
X,)) STM((X)) HF)(X,), CLS), +I]I
where "I5==1" represents stacking J times, and S-Fus(-) denotes the process of S-Fus module. Fur- thermore, eliminate motion-irrelevant cues, CCMD is applied to the features Fj obtained after domain-specific modeling. This block forces tri-domain modeling to disentangle essential compo- nents from motion inrelevant parts in generation features by leveraging causal loss Lief,s to guide gradient-based optimization. Importantly, CCMD is utilized exclusively during training.
3.3 TRI-DOMAIN MODELING FOR GENERATION
In this subsection, we introduce the five core components of the TriC-Motion Denoiser Block, i.e. TME, STM. HFA, S-Fus, and TIJ, as their details are shown in Fig. 4.
Temporal Motion Encoding, TME leverages vanilla TransforneeEncoderLaayg that attends over motion frames along the temporal dimension, explicitly capturing both short- and long-range depen- dencies for temporally coherent motion modeling. The output of TME can be expressed as:
plemp TranfomerfEncoderLyyry..
Spatial Topology Modeling, The skeleton of human motion sequence at each frame can be naturally represented graph, with joints as nodes and body segments as edges. To efficiently capture local
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"A pesson walks forward, sits."
TriC-Motion Denoiser Block
(O.1)
Texrral Information Injection
Aismpto Lp
IriC-Motion
X
Diffuse
Score-guided Tri-domain Fusion
Causal Intervention Block
HFA
CCMD
X
Denolser Blocks
DifTise
SIM
CCMD
Fusion
X
CCMD
x
TriC-Mation
Denoiser Block
Motion Enc.
ML.
DistiBERT
X
person
(a)
(b)
"A person walks forward, sits."
Figure 3: Overview of TriC-Motion. (a) Sampling process with stacked TriC-Motion Denoiser
Blocks. (b) Overall architecture the TriC-Motion framework.
pfre
SHF
IWWT
SHF
Ponling
Temp.
ConviD
GELU
ConviD
GELU
IFFT
Low-FFree Branch
High-Freq. Branch
S
Point-wise
Poaling
Joint
ConvID
G2
Lincar
Depth-wise ConviD
Depth-wise
ConviD
DWT
Sir
Shr
plemp
Factual Module
Linear
Softmax
Motion Scoring
pfrng
Semantic Scoring
Countie--
Linear
CLS
(d)
(c)
Figure 4: Detailed architectures of TriC-Motion main components, (a) HFA with DWT/FFT de- composition; (b) Low-frequency branch network in HFA: (c) nigh-frequency branch network in HFA; (d) S-Fus with motion and semantic scoring; (e) Details of CCMD.
topology and inter-joint dependencies, the STM module is built upon a graph convolutional network (GCN) (Kipf, 2016), enhancing the naturalness and physical plausibility of generated motion. In STM, a 3-layer GCN network is employed to model the joint dimension Xy:
pT X; + [LN[GELU(GCN(X)))))
=
where "I3"" represents stacking 3 times, and LN(-) denotes layer normalization.
Hybrid Frequency Analysis. Human motions exhibit distinct frequency characteristics: low- frequency components capture global motion trends, while high-frequency components reflect in- stantaneous changes and fine-grained details. Inspired by this, we propose the Hybrid Frequency Analysis (HFA) module (Fig. 4(b)), which combines Discrete Wavelet Transform (DWT) and Fast Fourier Transform (FFT) to leverage their synergy, capturing localized temporal-frequency dynam- ies via wavelet decomposition and global frequency patterns via Fourier analysis (Kiruluta & Lemos, 2025). Specifically, the motion sequence X, decomposed into low-frequency sub-band ŠT.F and high-frequency sub-band SHF via DWT, followed by FFT applied to ŠI.:
($E., SHF) DWT(X,). and SLF FFT(Š,r).
(4)
Utilizing the distinct characteristics of low- and high-frequency components, specialized branches designed for targeted analysis. As shown in Fig. 4(c), the low-frequency branch adaptively regions to highlight critical motion patterns. Two parallel convolution
attends to key networks respectively applied to SLF across temporal and joint dimensions to extract global
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contextual information. This information is further used to generate 2D spatial-tempon attention matrix to optimize the low-frequency signal, and the whole process can be expressed as:
(5) The high-freeuency branch employs lightweight convolutional architecture to capture fine- fine-grained local motion details. As shown in Fig. 4(d), utilizes two ID depth-wise convolutions (-) to extract spatial and temporal spectral information, which are then integrated through ID point-wise convolution (-) to enhance the high-fre signal SHF:
SLF = L F + Linear(SLF @ (we @ w.)).
S = SHF + (f (S#F)))
(6)
where GN(-) is the Group Normalization (Wu & He, 2018). After conducting analysis the low- frequency and high-frequency branches, the enhanced features SLF and S'e are obtained. Finally, (-) and I-W are applied to merge and transform them back the spatial-temporal domain.
Score-guided Tri-domain Fusion. As shown in Fig. 4(d), the S-Fus module employs a dual- branch scoring framework to integrate tri-domain features using global semantic context and mo- tion features. It consists of two scoring branches: Motion Scoring and Semantic Scoring. The former produces motion logits logitsmot to capture multi-domain correlations, while the latter leverages the global semantic token CLS to generate semantic logits logits ensuring seman- tic consistency during fusion. Afterward, the two logits are normalized via softmax to obtain domain-specifie weights ag, which are used for weighted fusion of tri-domain features Y,. The above process can be expressed as Eq. 7, where i E {temp, spa, freq). Here, F) denotes CAT(F F F./re). Both fmot(.) and feem(-) share the same feed-forward architecture, implemented as Linear( (ELLU(Linear()))
logits mot fmot (1 ), and logits'em feem(CAT((F) CLS))
Q; = Softmax (logitss + llogits
Y, Linear(CAT(X]. Σ Ea(F))).
3.4 CAUSALITY-BASED COUNTERFACTUAL MOTION DISENTANGLE
The CCMD module is applied to each layer of TriC-Motion Denoiser disentangle motion- irrelevant cues from beneficial causal contributions in tri-domain modeling. As shown in Fig.4, it includes two key components: the Factual and Counterfactual Modules. The two modules adopt a lightweight symmetric architecture to efficiently extract causal contributions Ej and confounders Cj from Fj. Taking Factual Module example, the detailed network can be expressed as:
E RIxIXD
(8)
E Linear Linear(wF)) (wF)) @ Fj
where (ool(-) AvgPool(.) + MaxPool(-), and the counterfactual features Cj can be obtained similarly. Next, we eliminate confounders by performing supervised causal intervention, calculated as Fj = -Wa Wa.Ci. where Wa, is implemented via linear mappings.
3.5
LOSS FUNCTION
Our model uses Lsimple as the primary training objective for motion generation, where the denoising process predicts the clean motion sequence in [ 4, c) from its noisy counterpart:
Csimple E
||000 f f, ]]]
(9)
where e and wi denote the prompt condition and the ground truth motion sequence, respectively. In addition, we introduce the Factual and Counterfactual Loss Liefs to constrain each CCMD module. In the j-th layer, the CCMD module produces tri-domain F, by concatenating features from three domains. This loss enforces the results of tri-domain causal intervention to approach the ground truth, thereby supervising CCMD to eliminate motion-irrelevant cues:
Liet = Σ wi WyCfess
Σ my (MME(TDEE,,0)
(10)

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Table 1: Quantitative results on H13anML.3D. The right arrow -> means the closer real motion the better. Each experiment is repeated 20 times, with average results and 95% confidence intervals (#) reported. The best result is highlighted in bold, and the second-best is underlined.
R- R-Precision f
Methods
FID
MM Dist J.
Diversity -
Top
Top 2
Top 3
GT
0.511 1
0. 0.703
0.7971.002
0.002
2.974
9.503
MDM (Tevet et al., 2022)
0.320
0.198:t.004
0.611 0.611#.000 #.007
0.544
6.566+.022
9.559
M2DM (Kong et 2023)
0.197#0003
0.682#:002 002
0. 0.763
0.352#
3. 3.131#
9.926
CoMo (Huang et al., 2024b)
0.502#-002
0.692 007
0. 0.790
0.2624.004
3.0324.015
9.936
StableMoFusion (Huang al., 20244)
0.553 -00
0.74g
0.841 #.002
0.09g
9.748th.002
BAMM (Pinyoamuntapong al., 20124)
0.525 2 002
0.720 000
0.814 -..144.003
4.054.000
9.7174.000 0e9
MoGinTSS (Yuan et al., 2024)
0.529 003
0.71g
0.812#.002
0.033 buggt.001 001
2.867
2.067
9.57
MoMask (Glun et al., 2024)
0.521
0.713 002
0.8074.000
0.045 4.002
2.958
MotioaPCM (Jiang al., 2025)
0.500 0022
0 000
0.814
0.040+.0m1
2.719+.00s
9.6324.089
MARDM (Meng et 2024)
0.500=20004
0.659
0.795=t-008
0000
0.114 2
3.270=t-009
MotionL.CM-v2 (Dai et al., 2024)
0.5512.003
0.745
0.836:t.002
0.049
003
7.00
9.584
LaMP (Li ct al., 2024c)
0.5572-003
0.75 20000
0.843:t.001
0.032 00002
2. 2.759
9.571
GMMotion (Tu et al.)
0.572#.003
0,761#
O. 0.852:::001
0.086:1003
2. 2.74331-008
9.7922t-08
SALAD (Hong et al., 2025)
0.581
0. 0.76g=t000
0.857#.002
0.076
2.619
9. 9.696-.000
TriC-MMtion (Ours, hase)
0.607 003
#.800
0.886 $8864.004
0.347
2.463#.01
9.428-4.08
TriC-Motion (Ours, large)
0.612 000
0.806
0.885
0.2854.022
2.465
9.434 0eg
Table 2: Quantitative results on SnapMoGen test dataset.
R-Precision
Methods
FID 1
CLIP Score
Diversity
Top
Top 2
Top 3
0.94115.001 001
0.976 001
0.985 #.00
0.001#0000
0.837#.000
19.756 7477
MDM (Tevet et al., 2022)
0.503 0022
0.653
0.727 0022
57.783 002
0.481 2-001
T2M-GPT (Zhang al., 2023a)
0.618:h-002
0. 0.773
0.812
32.62922-057
0.573
StubleMoFusion (Huung al., 2024a)
0.67g
0,823
0.858 1og--000 -002
27.801 #.063
0.605#.001
MARDM (Meng et ul., 2024)
0.65g
0.812 .02
0. 8.8600 #60
26.878 78.1118
0. 6000
MoMask (Guo et al., 2024)
0.777#.002
0.888
0.927
17.404
0.664#.001
MoMask+ (Guo et al., 2025)
0.802
0.905 .000
0.938
15.060
0.685
19.970 #048
TriC-Motion (Ours)
0.907#
0.969 S. 0010
0.985
26.316
0.675
19.831 # 042
where Wy represents the weight for each layer, with values (.21, 0.3, 0.4) when J 4. To improve the perceptual quality of generated motions, we augment Ssimple with a perceptual loss Cp. following the common practice in image processing (Johnson al., 2016). It operates in learned feature space to better capture semantic motion characteristics. Using pre-trained motion encoder (Guo et al., 2022; 2025) E, we extract feature representations from generated motion 2o and ground truth zo- The perceptual loss is defined as L.2-norm of their feature difference: Sp ||E(ā)) E (xo))l ensuring the generated motions are perceptually indistinguishable from real ones, enhancing overall quality. The total loss is defined in Eq. 11, with Ajef 1 and Xp = 10.
L = Lsiple + AJees + Ppp
(11)
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. We evaluate TriC-Motion on two large-scale motion-language benchmarks: Hu- manML3D (Guo et al., 2022) and SnapMoGen (Guo et al., 2025). HumanML3D contains 14,616 motion sequences and 44,970 texts aggregated from AMASS (Mahmood et al., 2019) and Human- Act12 (Guo et al., 2020), covering diverse activities (e.g.. walking, acrobatics). SnapMoGen com- prises 20,450 motion clips, each paired with six textual descriptions (two manually annotated and four LLMM-augmetted), totaling 122,565 descriptions with average length of 48 words.
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IriC-Motion (Ours)
MARDM
SALAD
MotionStreamer
walks forward with left hand above head.
X
X
A man raives the left arm while lowering the right arm, then switches the armz, and finally stands still.
The person sprints forward, suddenly stops, turns the right, and then sit down the ground.
A person crouches low, crawts forward, stands up quickly, steps to the right, and then waves both hands excitedly.
Figure 5: Qualitative comparisons HumanML3D dataset.
Evaluation Metries. We adopt the same evaluation metrics established (Guo et al., 2022; 2025): (1) Fréchet Inception Distance (FID), which measures the distribution distance of the generated and ground-truth motions; (2) Precision (R@ @@2, &R@3), Multimodal Distance (MM-Dist) and CLIP Score, which evaluate the semantic alignment between the input text and the generated motions; (3) Diversity, which measures the variability and richness of the generations.
Implementation Details. The diffusion timesteps are set to 50 with cosine variance schedule for 3, The number of denoiser blocks and feature dimension D are set to and 256, respectively. Two variants of TriC-Motion are implemented, with the temporal downsampling factor set to 7 for the base model and 4 for the large model. Classifier-free guidance is applied with scale = 4.0. The NVIDIA RTX 3090 GPUs with batch size of 64. AdamW is employed as
model is trained the optimizer with learning rate 1 X 10 10-4 Total training iterations are 650K for HumanML33D and 250K for SnapMoGen. Additionally, for both datasets, apply the same approach to reconstruct the pose sequences, obtaining a spatial-temporal 2D format of dimension M x N x 12.
4.2 EXPERIMENTAL RESULTS
Quantitative Comparison. We compare model with several existing state-of-the-art meth- ods on both HumanML.3D and SnapMoGien datasets, with the results summarized in Tab. and Tab. 2, respectively. Our method significantly outperforms all previous approaches in terms of R- Precision and MM-Dist, surpassing the second-best method, SALAD (Hong et al., 2025), by +0.031 184481 in MM-Dist, respectively, highlighting substantial improvement of TriC-Motion
in R@1 and 10.184 in text-motion alignment. Furthermore, when confronted with more complex and lengthy prompts SnapMoGen, our method maintains robust text consistency, markedly outperforming the second- best method, MoMask.+ With respect to FID, our approach achieves substantial reduction relative to the baseline on both datasets, indicating improved motion fidelity.
Qualitative Comparison. Fig. 5 compares our method with MARDM (Meng et al., 2024), SALAD (Hong et al., 2025), and MotionStreamer (Xiao et al., 2025). Across examples, the competing methods struggle to accurately follow fine-grained textual instructions. MARDM fails to follow multi-step instructions and produces luw-quality actions (e.g.. "sit down" and "crawl") with unrealistic body structures. SALAD and MotionStreamer improve overall motion smoothness and visual quality, yet both still struggle to adhere to fine-graine cues and have difficulty producing complete and coherent sequences for long, compositional prompts. For instance, key actions such
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Table 3: Ablation study of the proposed modules in TriC-Motion on HumanMM.3D test dataset, as well as the analysis of HFA. "2D rep" denotes the spatio-temporal 2D motion representation with dimensions M N 12. "low" and "high" " denote low- and high-frequncy modeling in HFA. "w/o joint" means removing the joint-wise frequency branch while retaining the temporal one.
R- R-Precision f
Method
FID
MM Dist
Diversity
Top
Top 2
Top
GT
0.511 4..03
0.703 o0s
0.797 000
0.002-4000
2.974
4.5034.005
Baseline
5.320
0.44
0.611=.007
0.544
5.566
027
9.5592-000
TME (2D rep)
4.470
0.671#.005
0.671 40000
0.777
2.710
3.293 023
8.016+4.004
TME STM
0.5704.004
+.771
0.771 #0005
0.859 005
0.611 0.6114.006 4.00G
2.6174-018
9.1574.120
TME HEA
0.583
0.778
0.869 .004
0.374
2.576+
9.535+.077 072
TME STM HIA
0.502:$.006
0.780
0.867=.000 000
0.383 4.2e
2.5644
9.67944.092
TME + STM + HFA S-Fus (Ours)
0.6072.030
0.800 8.800..004
0.886
1.004
0.347 2.011
2.4634
#..02
9. 9.4284.085
w/o HFA
0.5724.004
0.765+.004
0.863 -004
0.5934.002
2.599+.022
9.213#t-08
w/o FFT
0.595
0.790
0.877=-004
0. 1-400
2.518
9.335
w/o joint
0.50g 008
0. 0.793 005
0.881 000
0.418:t-085
2.527 018
9.188#+100
w/o High-bund
2:00
0. 0.799 000
0.885 2005
0. 0.50
2t494
9.029+.080
w/ low + high (Ours)
0.6 0.60722000 005
0,800 0.800--004
0. 0.886
0.347 051
2.463
2:0012
9.428 085
Table 4: Ablation experiment of CCMD (upper half) and the layer-wise loss weights of Let (lower half) on HumanML33D, "pre" means that applying causal intervention before S-Fus, whereas "post" employs it afterward. "temp" " denotes applying CCMD only to the temporal domain, while "temp+spa" " denotes applying CCMD jointly to the temporal and spatial domains. {wi, W2, W3, W4 denotes the weights for Lief when = 4.
R-P R-Precision
Method
FID
MM Dist
Diversity
Top 1
Top
Top 3
GT
4:003 4.511
0.511
0.703 003
4.797
0.002 con
2.974 0.008
9.50g
w/ CCMD
0.568
0.767 007
0.859
0.561 th
2.624 4.003
9.187
w/ CCMD (post)
0.60g 0s9
0.798 007
0.880 008
0.328
027
2.512
9.482 008
w/ CCMD (lemp)
0.580 008
0.773 00s
0.85g
0.514
2.617 2.000
9. 9.467
w/ CCMD (lemp + spa)
#.00s
0.792 006
0.875 t.00
0.875
GB4 0.329
9.345 2.52
2.525
h.010
w/ CCMD (lemp spa freq. Ours)
0. 0.607
0. 0.800
0.886
0.347 031
2.463
9.428
{0.0, 0]
0,580
0.773
0. 0.860
0.460 000
2.621 2.621 #.013
9. 0.0334.088
{0.25, 0.25, 0.25, 0.25)
0.582 004
0.7 000
0.867 004
0.478+.062 4.22
2..14
9.201 4.002
(0.1, 0.2 0.3, 0.4) (Ours)
0. 0.607:.000 00s
0.800
0.886 004
0.347= 033
2.463 0022
9.428 os5
as "waving" are often omitted. Moreover, all three methods struggle with derection-related prompts (e.g., "forward"" "right") and left-right limb coordination, leading to motions that deviate from the specified details. In contrast, our method achieves higher-fidelin motion generation and consistently aligns with complex, fine-grained prompts.
4.3 ABLATION STUDY
Effectiveness of Tri-domain Modeling. As shown in the upper part of Tab. 3, we progressively add STM, HFA, and S-Fus on top of TME to reveal each module's contribution. When S-Fus is not used, tri-domain features are simply concatenated. Introducing STM or HFA alone already brings clear gains. STM improves R @ 1 by about 0.1 and reduces MM-Dist by enforcing realistic joint topology, while HFA yields the largest FID drop by modeling global trends and fine-grained dynamics via low- and high-frequent describe complementary aspects of motions, combining STM and HFA further enhances semantic alignment and fidelity. Building these foundation, the full framework with S-Fus achieves the best performance, improving R@ 1 to 0.607, by adaptively weighting domain-specific cues rather than relying on simple concatenation.
cues. Since spatial structure and frequency characteristics
Impact of Frequency Components (Low vs, Low + High). The lower part of Tab. 3 validates the effectiveness of the internal components of HFA. Removing the FFT branch results in clear
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Table 5: User study results.
Method
Text-Motion Alignment
Overall Quality
MARDM (Meng al., 2024)
2.9704.108
3.268
MoMask (Guo et al., 2024)
3.096
3.289 103
SALAD (Hong al., 2025)
3.5184.100
3.567- 009
Ours
4.125
3.981 4.091
decline in both semantic alignment and motion fidelity, indicating that modeling the low-frequency components within hybrid FFT-DWT frequency space enables the network to better capture global motion structures. Limiting HFA the temporal domain ("w/o joint") further degrades multiple metries, demonstrating that joint-domain frequency modeling is crucial for capturing fine-gras fine-grained spatial dynamics and ensuring coherent multi-joint coordination. Compared TriC-Motion without HFA, using only the low-frequency band improves @@ to 0.602, MM-Dist to 2.494, and reduces FID. Combining both frequency components achieves the best performance, optimizing coarse mo- tion trends and fine-grained dynamics, further confirming HFA's capability for faithful and diverse motion generation.
Ablation of Causal Intervention. As shown in the upper part of Tab. 4, removing CCMD signifi- cantly degrades R-Precision and increases FID, demonstrating CCMD's role in guiding the model to reduce motion-irrelevant cues, thereby improving generation quality. Applying CCMD solely to the temporal domain yields measurable improvements text-motion alignment, with R@ enhanced to 0.580. Extending CCMD to both temporal and spatial domains further enhances text-motion alignment and reduces MM-Dist, achieving performance comparable to the "pos!" variant. TriC- Motion with full tri-domain configurations achieves the best performance, confirming that causal intervention is most effective when jointly applied across all three domains.
Ablation on Liser-wise Loss Weights of Lfef- We conduct an ablation on the layer-wise weights of Liet. with results shown in the lower part Tab. 4. The progressively increasing schedule {0.1, 0.2. 0.3, 0.4) attains the best results. This demonstrates that assigning larger weights to deeper denoising blocks can suppress domain-speciio confounders and provide causal guidance more ef- fectively, resulting in more stable denoising process and better overall performance.
4.4
USER STUDY
To validate the perceptual quality of motion generation, we conduct a user study following the protecol adopted by GMMotion (Tu et al.) and SALAD Hong et al. (2025). In this study, 38 par- ticipants compare our method against three representative baselines: MARDM (Meng et al., 2024), MoMask (Guo et al., 2024), and SALAD (Hong et al., 2025), which represent MAR paradigms, discrete AR models, and continuous diffusion frameworks, respectively. For each method, partici- pants are presented with 15 video examples and evaluate them based on criteria: Overall Quality and Text-Motion Alignment. All ratings are collected using a 5-point Likert scale ranging from (poorest) (best). The results, summarized in Tab. 5, demonstrate that the motions generated by TriC-Motion are more preferred by humans in terms of overall visual quality and text-motion consistency compared to other methods.
5 CONCLUSION
We propose TriC-Motion, a novel diffusion-based framework for text-to-motion generation that inte- grates temporal, spatial, and frequency modeling with causal intervention. This framework features three modules, TME, STM, and HFA, for comprehensive domain-specific modeling. S-Fus then adaptively integrates these domain-specific cues, while TI injects semantic information into mo- tion features. Furthermore, the framework incorporates CCMD to perform causal intervention dur- ing training, which suppresses motion irrelevant cues caused by noise while enhancing real causal contributions. Extensive experiments demonstrate the state-of-the-an performance of TriC-Motion, particularly in R-Precision and MM-Dist, highlighting its powerful motion-generation capability.
motion-irrelvaan
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6 ACKNOWLEDGEMENT
This work was supported by National Natural Science Foundation of China (No. 62376102) and Ant Group.
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A APPENDIX
A.1 ETHICS STATEMENT
This work adheres to the ICLR Code of Ethics. this study, no human subjects or animal experi- mentation were involved. All datasets used, including HumanML3D and SnapMoGen, were sourced in compliance with relevant usage guidelines, ensuring no violation of privacy. We have taken care to avoid any biases discriminatory outcomes in research process. No personally identifiable information was used, and no experiments were conducted that could raise privacy or security con- cerns. We are committed maintaining transparency and integrity throughout the research process.
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Komma ber
proking
Figure Al: t-SNE Visualization of the the three feature types in TriC-Motion: motion-relevant fea- tures Fy, motion-irrelevant (confounding) features Pef. and the final causally disentangled features Fide used for generation. The six text inputs are taken from the HumanML3D test set.
Table Al: FLOPs and average inference time (AIT) comparison across various methods.
Method
FLOPs (G)
AIT (s)
MDM (Tevet al., 2022) (DDIM--0step)
325.25
1.5
SALAD (Hong et al., 2025)
233.83
3.0
MARDM (Meng et al., 2024)
23519.50
10.6
MoMask (Guo et al., 2024)
37.50
0.4
Ours
388.45
3.8
A.2 REPRODUCIBILITY STATEMENT
We have made every effort to ensure that the results presented in this paper are reproducible. All the code will be released after the double-blind review process to ensure that the results can be reproduced and verified. The experimental setup, including training steps, model configurations, and hardware details, is described in detail in the paper. Additionally, the HumanML33 and SnapMoGen datasets publicly available, ensuring consistent and reproducible evaluation results. We believe these measures will enable other researchers to reproduce our work and further advance the field.
A.3 LLMS USAGE STATEMENT
This paper utilizes Large Language Models (LLMs) solely as a tool for polishing the texts, thereby refining the paper and improving readability. All ideas, research concepts, research methodology, experiments, figures, and visualizations are conducted without the use of LLMs.
A.4 T-SNE VISUALIZATION OF CCMD
As shown in Fig. A1, we present t-SNE visualizations of six different text inputs, from the H3mDnML3D test dataset, processed by TriC-Motion, showing the motion-relevant features Fj. motion-irreled irrelevant features Fef. and the final features contributing to generation Fide obtained through causal modeling, As shown in the t-SNE visualizations on our website, for certain complex or easily confusable motion texts, their features Fy overlap in the space. After heing modeled by the CCMD module based on causal learning, the disentangled features Fide clearly demonstrate that the features corresponding each text become separated, and features within the same category are more clustered. This indicates that after removing the confounding information and motion-relevant factors, the model generates more accurate and less ambiguous motion features based on the text, thereby improving the consistency between generated motion and text as well as the fidelity of the motion.
A.S COMPARISON OF COMPUTATIONAL COST, INFERENCE TIME AND MODEL PARAMETERS
As showin in Tab. A1, present the FLOPs and average inference time (AIT) measured over 100 samples on a single NVIDIA RTX 3090 GPU for several representative methods. Parameter sizes summarized in Tab. A2, TriC-Motion requires 388.45 GFLOPs, which is only slightly
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Table A2: Parameter size comparison across various methods.
Method
Params (M)
MDM (Tevet al., 2022)
17.88
MLD (Chen et al., 2023b)
26.38
ReMoDiffuse (Zhang et al., 2023b)
46.97
MoMask (Guo et al., 2024)
44.85
SALAD (Hong et al., 2025)
10.10
MARDM (Meng ct al., 2024)
310.09
Ours
13.86
Table A3: Sensitivity analysis of the perceptual loss Lp (upper part) and causal loss L (lower part). Here, and 3 denote the weights of Lp and faf, respectively. The default setting (Ours) corresponds to a 1.0, 3
R-Precision f
B
FID /
MM-Dist J
Diversity +
Top
Top
Top 3
1.0
10
0.607
0.800 +.04
0.886 0.04
0.347 h.011
2. 2.463 #.12
9.428 +208
1.0
1
0.597
0.787 .008
0.867
0.416 #.035 .03
2.571 4.023
9.917
1.0
20
0.600
0.794#t005
b.8
0.425##037
2.517
9.229-t074
0.5
10
0.609#-005
0.803#-004
0.882#-004
0.349#-032
2.485+-0177
9,464=-071
2
10
0.59
0.793
0,881 4.004
0.418 #.055
2.527
9.188 9.188 1007
higher than baseline (MDM), and achieves an AIT of 3.8 s, remaining comparable to latent- space diffusion models such as SALAD (3.0 s) while being considerably faster than MARDM (10.6 s). The model also highly compact, containing only 13.86M parameters, which is significantly smaller than many strong baselines including MoMask (44.85M), ReMoDiffuse (46.97M), MLD (26.38M), and MARDM (310.09M).
The additional computation primarily comes from explicit tri-domain modeling, which cannot be replicated by simply scaling single-domain network. Despite incorporating temporal. spatial, and frequency branches, the overall architecture remains deliberately lightweight. Furthermore, TriC- Motion conducts diffusion directly the raw motion space, where multi-step denoising inherently incurs higher cost. This is a well-known limitation shared across diffusion-based motion generators. Nevertheless, the proposed tri-domain design yields clear and consistent benefits, improving R @@) to 0.612 and enhancing motion fidelity on both HumanML3D and SnapMoGen.
is also worth noting that the causal module is used only during training and introduces no inference-time overhead, and all tri-domain branches efficient and parallelizable.
Prior works (StsbloMoFusion (Huang et al., 2024a) and -ight-TMM (Zeng et al., 2025)) demon- strate that advanced samplers (e.g., DPM-Solver (Lu et al., 2022). UniPC (Zhao et al., 2023)) can reduce diffusion to as few as 10 steps. yielding substantial acceleration with minimal quality degra- dation. These findings indicate that TriC-Motion can be efficiently accelerated using modern sam- pling strategies, and in future work we will further explore such techniques while also investigating diffusion in more compact latent space optimize the inference process.
A.6 SENSITIVITY ANALYSIS OF LOSS WEIGHTING
We perform a comprehensive sensitivity analysis on both the perceptual loss and the causal loss, and the results are presented Tab. A3. The findings consistently show that TriC-Motion remains stable under wide range of loss-weight configurations. When the weight of the perceptual loss varies from to 20, the changes in R- R-Precision remain minor, with values of 0.597 and 0.607, and the corresponding FID values vary only slightly from 0.347 to 0.425. No sign of instability or significant performance degradation is observed this process. A similar phenomenon appears when adjusting the weight of the causal loss from 0.5 to 2, where the resulting performance remains
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Table A4: The performance comparison on HumanMIL3D dataset between TriC-Motion without Cp and the previous state-of-the-an method, SALAD (Hong et al., 2025).
Method
R@ 1
R@21 f
R@3 f
MM-Dist L
GT
0.511 = 003
0.703 003
0.797
2.974#.0008
SALAD (Hong et al., 2025)
0.581 = 003
0.769 003
0.857#-002
2.649#-000
Ours (wo Ep)
0,585 S
0.772 1: 006
0.863
2.553-..029
Ours
0.607
0.800
0.886
2.463#-012
Table A5: Performance comparison HumanML3D dataset under the CLaM evaluator.
Methods
R@D
R@21 f
R@31
GT
0.738 2.000
0.870
0.917
MLD (Chen et al., 2023b)
0.599
0.760
0.831 = 002
MotionDiffuse (Zhang et al., 2024a)
0.645#-004
0.803#-003
0.868 003
T2M-GPT (Zhang al., 2023a)
0.676+.003
0.820#.004
0.878= 000
MotionGPT (Jiang et al., 2023)
0.178=t.002
0.655
0.752
T2M (Guo al., 2022)
0.5772-00s
0.730#-002
0.804=.002
MoMask (Guo et al., 2024)
0.715 4.002
t.856..000
0.909
SALAD (Guo et al., 2024)
0.776 77776#.000 000
0.902 002
0.943=#0001
Ours
0.786 0006
0.912 4.005
0.952 000
highly consistent. These observations indicate that the causal intervention branch does not introduce additional sensitivity and that the overall optimization robust. The results therefore confirm that TriC-Motion is not sensitive to the weighting of these loss terms.
A.7 ABLATION STUDY OF PERCEPTUAL Loss C, AND PROSS-EVALLATORN VALIDATION
We further analyze the role of the perceptual loss Lp to ensure that it does not induce unintended coupling with the HumanML.3D evaluator. The use of a perceptual objective in a learned feature space follows a well-established practice in generative modeling, where such objectives accelerate convergence and improve perceptual quality, particularly in diffusion-based image and video gen- cration. Although incorporating this perceptual signal indeed brings a moderate improvement, its presence is not the source of our R- R-Precision gains. As shown in Tab. A4, removing the perceptual loss still yields competitive results compared to strong baselines (e.g., SALAD (Hong et al., 2025)), including solid R-Precision and MM-Dist performance. This confirms that the central improvements originate from our tri-domain modeling and causal intervention design, rather than from coupling between the loss and the HumanML3D evaluator.
We additionally evaluate the same trained model using CLaM (Chen et al., 2024b), more advanced text-motion evaluator whose embedding space is entirely different from the one used during train- ing. This setup eliminates any possible overlap between the perceptual loss feature space and the evaluation space. As shown in Tab. A5, despite the shift this substantially stronger evaluator, our method consistently achieves superior R- Preriisi,,,, surpassing both the ground truth and strong baselines such as SALAD (Hong et al., 2025) by a large margin. Even under the condition where different evaluators are used during training and inference, these performance improvements persist. This demonstrates that our performance gain does not rely on the alignment between the perceptual loss space and the evaluation feature space, nor does it stem solely from the perceptual loss term. Instead, it originates from the combined innovative design of tri-domain modeling, causal learning, and other contributions.
A.8 SUPPLEMENTARY VISUAL MATERIAL
To facilitate comprehensive evaluation, we provide a dedicated website hosting all supplementary qualitative materials, including motion visualizations, side-by-side comparisons with state-of-the-
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Published as a conference paper at ICLR 2026
These materials complement the main paper by offering additional perspectives on motion fidelity and semantic alignment. Importantly, the website strictly adhere to the double-blind review principles of the ICLR 2026 conference, ensuring the anonymity of the website.
art methods, and t-SNE visualization: https:/sites. wttpoo/...oo./iiiiiimmttttoooooa
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Reviews:
[{"review_id": "daaxxcTAXT", "reviewer": "Reviewer_CRLx", "summary": "This paper proposes a 'tri-domain + causal' framework for text-to-motion generation. Built on MDM, it models motion in parallel with TME (temporal encoding), STM (skeleton-topology GCN), and HFA (hybrid frequency analysis via DWT+FFT). A dual-score S-Fus module fuses motion/semantic signals, and TIJ injects text via cross-attention. Training uses CCMD counterfactual decomposition (factual + counterfactual branches) to suppress spurious cues.", "questions": "Please kindly refer to the weaknesses mentioned above.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The proposed method achieves remarkable improvement on the R Precision metric.\n\n2. The paper is well-written, ensuring that its content is easily understandable for readers.\n\n3. It is the first time for casual learning to be used in text-to-motion generation, making significant contributions to the research community.", "weaknesses": "My primary concern is the choice of baselines. Under the HumanML3D evaluation protocol, the evaluator is too weak: many recent methods already surpass the 'ground truth', making R-Precision on HumanML3D unreliable. Meanwhile, the FID gap to stronger methods is large (0.285 vs 0.033), so the proposed method shows no advantage on HumanML3D. Porting the approach to a MoMask baseline should not be difficult; the authors should adopt a more appropriate baseline; otherwise, it may look like trading motion quality for text consistency, which does not substantiate effectiveness.\n\nIn addition, the ablation should include more combinatorial settings to better demonstrate effectiveness.\n\nFinally, the paper lacks a demo video as supplementary material and a user study to subjectively assess text-motion alignment and motion quality.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uI0TZ5ZZ1R", "reviewer": "Reviewer_7LNK", "summary": "This paper proposes TriC-Motion, a diffusion-based framework integrating spatial-temporal-frequency-domain modeling with causal intervention. It includes Temporal Motion Encoding, Spatial Topology Modeling, and Hybrid Frequency Analysis, with a Score-guided Tri-domain Fusion and a Causality-based Counterfactual Motion Disentangler to expose motion-irrelevant cues and fuse valuable information across domains.", "questions": "Refer to Weaknesses.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 4, "contribution": 2, "strengths": "1. The first work that simultaneously integrates spatial, temporal, and frequency domains into a unified motion generation framework\n\n2. Introduces a causality-based counterfactual motion disentangler to expose motion-irrelevant cues and disentangle the real modeling contributions of each domain.\n\n3. Provides ablation studies indicating the effectiveness of each domain branch and the causal-intervention design.", "weaknesses": "1. The paper uses a perceptual loss defined in the same motion–text embedding space used by the HumanML3D evaluator (the author could clear this point if I'm wrong). Using the same feature extractor for training and inference would inflate the performancve. The author could do an ablation study that removes this loss term to show that the R-precision gain is not from this loss term.\n\n2. No visualization results. Quantitative metrics in text-to-motion is proven to be fragile and sometimes misaligned with human judgment. For motion, demo videos are necessary. I don’t see any supplementary videos, which makes it hard to judge the actual quality.\n\n3. The metrics themselves aren’t convincing. Even if R-precision is SOTA, an R-precision much higher than the ground truth is not meaningful and doesn’t reflect visual quality. Even worse, FID is significantly poorer compared to current methods.\n\n4. Missing strong baseline results. Compare against recent, stronger works (MARDM [1], MotionStreamer [2], MotionLCM v2 [3], SALAD [4]) with both qualitative and quantitative results. Also consider including a human study.\n\n5. Minor typo. In the introduction you say R@1 is 0.573, which doesn’t match Table 1.\n\nReference\n\n[1] Rethinking Diffusion for Text-Driven Human Motion Generation\n\n[2] MotionStreamer: Streaming Motion Generation via Diffusion-based Autoregressive Model in Causal Latent Space\n\n[3] MotionLCM-V2: Improved Compression Rate for Multi-Latent-Token Diffusion\n\n[4] SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and Editing", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "jGp96avLs2", "reviewer": "Reviewer_QW9h", "summary": "This study proposes TriC-Motion, a diffusion-based framework for text-to-motion generation that integrates spatial, temporal, and frequency domain modeling with causal intervention to ensure temporal consistency, spatial topology, and dynamic coherence.\nExperimental results show that TriC-Motion achieves an R1-Precision of 0.612 on the HumanML3D dataset, significantly outperforming existing methods and generating motions with superior realism, consistency, diversity, and text alignment.", "questions": "See Weaknesses.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The proposed method demonstrates strong text–motion consistency.\n\n2.The introduction of causal learning reduces the impact of irrelevant information on motion generation.", "weaknesses": "1.Please provide t-SNE or other visualization analyses that disentangle motion-irrelevant and motion-relevant information to demonstrate the effectiveness of the proposed method.\n\n2.The paper’s joint temporal–frequency–spatial strategy improves text–motion alignment (R@1, R@2, R@3), but FID did not improve; therefore you cannot claim that generation quality has improved, and the conclusions stated in the abstract are not supported.\n\n3.What advantages does using DistilBERT for word-level and sentence-level feature encoding have compared to CLIP?\n\n4.The proposed TME, STM, HFA, and S-Fus are commonly used extraction and fusion strategies in the temporal–spatial–frequency domain and lack novelty.\n\n5.The methods compared in Figure 5 are all general approaches; it’s unclear whether the proposed method outperforms contemporary spatio-temporal modeling methods. If possible, provide qualitative comparisons (or quantitative comparisons if allowed) against methods from papers accepted to CVPR 2025, ICCV 2025, and NeurIPS 2025.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OCh9Otmso7", "reviewer": "Reviewer_p1bB", "summary": "The paper introduces TriC-Motion, a new diffusion-based framework for text-to-motion generation that unifies spatial, temporal, and frequency-domain modeling under a causal learning paradigm. Authors address two limitations of existing approaches: 1) the lack of an integrated multi-domain representation that captures temporal dynamics, spatial joint topology, and motion frequency characteristics simultaneously, and 2) the entanglement of motion-relevant and irrelevant cues that degrade generation quality. To overcome these issues, TriC-Motion introduces three key components: Tri-Domain Modeling Modules, Score-guided Tri-domain Fusion, and Causality-based Counterfactual Motion Disentangler. Experimental results on HumanML3D and SnapMoGen demonstrate good performance gains over state-of-the-art baselines.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ The performance of the proposed method on both HumanML3D and SnapMoGen dataset surpasses most baseline methods, proving the effectiveness of modeling all three domains.\n+ The paper writing is clear and easy to follow.\n+ The exploration of causal intervention in motion generation domain is interesting and inspiring.", "weaknesses": "- The main idea of modeling all three domains at the same time is less convincing and requires more intuitive explanation and theoretically grounded analysis.\n- The architecture is heavy in computing and complex in the architecture. Therefore, it is important to also compare the run time, computation cost and model size with baseline methods.\n- The training stability is also worrying due to the complex architecture. The loss weighting needs more deep analysis and sensitivity test.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)