Event-T2M: Event-level Conditioning for Complex Text-to-Motion Synthesis
Authors: Seong-Eun Hong
Jaeyoung Seon
JuYeong Hwang
JongHwan Shin
HyeongYeop Kang
Venue: ICLR 2026 Poster (2026)
Keywords: text-to-motion generation
event-level conditioning
event decomposiiton
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Text-to-motion generation has advanced with diffusion models, yet existing systems often collapse complex multi-action prompts into a single embedding, leading to omissions, reordering, or unnatural transitions. In this work, we shift perspective by introducing a principled definition of an event as the smallest semantically self-contained action or state change in a text prompt that can be temporally aligned with a motion segment. Building on this definition, we pro- pose Event-T2M, a diffusion-based framework that decomposes prompts into events, encodes each with a motion-aware retrieval model, and integrates them through event-based cross-attention in Conformer blocks. Existing benchmarks mix simple and multi-event prompts, making it unclear whether models that succeed on single actions generalize to multi-action cases. To address this, we con- struct HumanML3D-E, the first benchmark stratified by event count. Experiments on HumanML3D, KIT-ML, and HumanML3D-E show that Event-T2M matches state-of-the-art baselines on standard tests while outperforming them as event complexity increases. Human studies validate the plausibility of our event definition, the reliability of HumanML3D-E, and the superiority of Event-T2M in generating multi-event motions that preserve order and naturalness close to ground- truth. These results establish event-level conditioning as a generalizable principle for advancing text-to-motion generation beyond single-action prompts. Code and data are available at https://tjswodud.github.io/EventT2M.

Published as a conference paper at ICLR 2026
EVENT-T2M: EVENT-LEVEL CONDITIONING
FOR COMPLEX TEXI-TO-MOTION SYNTHESIS
Seong-Eun Bung Hong'; Jae
Jae Young Seon?; Ju Hwang', wong Shin', HyeongYeop Kang"t
"Department of Computer Science and Engineering. Korea University
Department of Artificial Intelligence, Kyung Hee University (seongLeun, 05 judy02, citi1006, siamisa waw.miiz.aaggkooraaaaaoo kr!, cogongnam khu ac kr
ABSTRACT
Text-to-motion generation has advanced with diffusion models, yet existing sys- tems often collapse complex multi-action prompts into a single embedding, lead- ing omissions, reordering, or unnatural transitions. In this work, we shift per- spective by introducing a principled definition of an event as the smallest seman- tically self-contained action or state change in a text prompt that can be tem- porally aligned with motion segment. Building on this definition, we pro- pose Event-T2M, diffusion-based framework that decomposes prompts into events, encodes each with motion-aware retrieval model, and integrates them through event-based cross-attention in Conformer blocks. Existing benchmarks mix simple and multi-event prompts, making it unclear whether models that suc- ceed on single actions generalize to multi-action cases. To address this, we con- struct HumanML3D-E, the first benchmark stratified by event count. Experiments on HumanML3D, KIT-ML, and HumanMMLDDEE show that Event-T2M matches otate-of-the-art baselines on standard tests while outperforming them as event complexity increases. Human studies validate the plausibility of our event defini- tion, the reliability of HLmanML3D-E, and the superiority of EveettT2M in gen- erating multi-even motions that preserve order and naturalness close to ground- truth. These results establish event-level conditioning as a generalizable principle for advancing text-to-motion generation beyond single-action prompts. Code and data are available hhttps:/ htt/:/tssssuuuuu. github..... io /Eeeett22
INTRODUCTION
Text-to-motion generation has recently achieved striking numerical results on benchmarks such as HumanML33 (Guo al., 2022) and KIT-ML (Plappert et al., (016), with state-of-the-aas models pushing Fréchet Inception Distance (FID) to the second decimal place. However, these numbers ob- scure a critical limitation rooted in the benchmarks themselves. HumanML3D, for example, primar- ily consists of simple, easy-to-generate motions; performance on trivial motions rather than tackling the challenge of -egneeregeee In essence, the field has become adept at making simple motions slightly better while ignoring the complex, temporally ordered behaviors where text-to-motion could truly matter. Consequently, when de- scription such as "run forward, then stop, then wave" is given, leading systems frequently merge, skip, or reorder actions. This misalignment between benchmark success and the demands of struc- tured real-world motion remains a key obstacle to deploying text-to-motion techniques in practical applications such as animation pipelines (Kappel et (021). video production (Majoe et al. 2009 Yeasin et al. 2004). and embodied agents (Yoshida et al. 2025).
result, most research has focused on refining
To move beyond this impasse, it is necessary first to characterize compositional complexity rather than treat all prompts as equally difficult. Existing datasets and evaluation protocols do not dis- tingiss between simple single-action descriptions and complex multi-action sequences, making impossible assess whether improvements on low-complexity motions carry over to scenarios requiring higher temporal and structural complexity.
"These authors contributed equally to this work.
1 "orresponning author.

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Published as a conference paper at ICLR 2026
In this work, we make three main contributions. (1) We reframe text-to-motin generation around the notion of an event, introducing a principled definition of an event as the smallest semantically self-contained action state change described in text prompt whose execution be temporally isolated and mapped to a contiguous motion segment. (2) Building on this definition, we propose Event-T2M. This diffusion-based model that injects event tokens through novel event-based cross- attention module (ECA), enabling the generation of complex multi-action sequences and achieving state-of-the-art performance on HumanML3D and KIT-ML. (3) To rigorously assess whether gains on simple motions generalize to compositional cases, we construct and release HumanML3D the first benchmark that systematically stratifies text-to-motion prompts by event count, thereby introducing a reproducible evaluation protocol for event-level complexity and demonstrating the advantages of Event-T2M under increasing compositional demands.
2 RELATED WORKS
2.1 COMPLEX TEXT-TO- SYNTHESIS
Text-to-motion generation has made remarkable progress since the introduction of the Hu- manML3D (Guo et al. 2022). Research has mainly diverged into two directions: Vector Quantized- Variational AutoEncoder (VQ-VAE) (Van Den Oord et al. 2017) 2017)-based models and diffusion (Ho) al. bassed models, pioneered respectively by T2M-GPT (Zhang al.] 2023a) and Mo- DifuDe (Zhang et al. (2024a). VQ-VAE work has primarily focused on reducing quantization loss (Guo al. 2024). Meanwhile, diffusion-based approaches have concentrated
improving model performance while simultaneously reducing the inference time of the diffusion process (Chen 2023 Zeng al. 2025). Across both, the main objective has been higher scores on Hu-
al.
manML3D, with many works achieving state-of-the results.
Beyond these benchmark-driven gains, some diffusion-based studies explicitly target more complex behaviors. GraphMotion (Jin et al., (023) enriches text with semantic graphs to encourage compo- sitional generation, though its evaluation is limited. MotionMamba (Zhang et al. (024b) defines "complex" motions merely by filtering longer HumanML.3 sequences, offering limited insight into true compositionallit.
2.2 TOKEN-LEVEL CONDITION FOR FINE-GRAINED ALIGNMENT
Recent advances in text-to-motion increasingly adopt token-level cross-attention for fine-grained alignment between text and motion. A representative example is AttT2M (Zhong al.) (023), which combines body-part attention and global-local motion-text attention. Motion is first encoded into a discrete latent space using a VQ-VAE whose encoder preserves body-part structure, ensuring token interactions reflect part-level dependencies. During generation, local cross-attention links mo- tion tokens with individual words, while global attention via sentence embeddings provides holistic guidance. This design improves interpretability and motion quality, yielding strong results on Hu- manML3D (Guo et al. 2022) and KIT-ML (Plappert et al. 2016).
MMM (Pincoanuntapong et al. 2024) extends this line with masked motion modeling, reconstruct- ing motion tokens from masked segments conditioned on text. By jointly encoding text and mo- tion in a single transformer, MMM enables bidirectional attention, reinforcing token-level align- ment. Together, AuT2M and MMM demonstrate the benefit of token-level conditioning for richer mext-motion correspondences.
However, much of the literature still relies on CLIP (Radford al., 202 whose text encoder rep- resents an entire prompt with single global embedding (e.g., the [FOS] token) when performing image-text matching. This design obscures the temporal order of multi-step descriptions. For ex- ample, "run forward, then stop, then wave" may collapse into one undifferentiated vector, leading to merged or reordered actions. In addition, CLIP's pretraining on broad image-text corpora provides weak supervision for motion, overlooking temporal continuity and event transitions that critical for compositional generation.
To address these issues, leverage TMR (Text-to-Motion Retrieval) (Petrovich et al. 2023), trained explicitly for motion-languee alignment, injecting domain expertise absent in CLIP. Fur- thermore, instead of collapsing the entire prompt into one token, we introduce event-level tokeniza-
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Data Preprocessing
Event-T2M
Origiaal Text
person stowly walks forward with spine siihhly hunched forward, ther right and right walks
Pleckcied
Fusion
lit'
Mlle
I1te"
Event: based Gross sttention Convolation
forward again
Mull-Head Soll-Aitention
LIM
Local Information Mocialng Module
Adlapóive Textual Information injector
Clouses
Event- nenn-llvvll Conditionnng Module
sliwlly forward hunched
Local Information Modeiing Module
mi mi mi
mf
E2
Es
E4
Es
their rigtt forward the direction.
parson then ums around to their right
me
mr
m.t
Event-level
W
A person wakes forward again.
Molaan tokena:
Conditioning Module
Main Architecture of Event-T2M. An input prompt is split into clauses by LLM, en- coded as event tokens with TMR encoder, and fused with a global token. Tokens guide the diffusion process through event-level module, enabling generation of sequentially complex motions.
Figure 1:
representative tokens are extracted per event, allowing feature matching that preserves temporal order and enhancing robustness to sequentially complex motions.
tion:
METHOD
We design Event-T2M, diffusion-based text-to-motion generator tailored handle complex quential motions by explicitly modeling event structure. Our approach builds on three key ideas: (1) decomposing text into an event sequence using Large Language Model (LLM), (2) embedding each event into an event token via motion-specialized TMR encoder, and (3) injecting these event tokens through an event-based cross-attention (ECA) module inside Conformer (Gulati et al. 2020) blocks to capture both local and global sequencing.
3.1 TEXT EVENT TOKENS
We formalize an event as the smallest semantically self-contained action or state change described in natural-language prompt, whose execution be temporally isolated and mapped to tiguous segment of the target motion. This definition is inspired by prior work on temporal action segmentation (Jin et al. 2023).
Formally, a text prompt W is segmented into a sequence of clauses {C*) by an LLM, where K denotes the number of clauses obtained under fixed linguistic rules. clause Ce mapped to an event if it (1) expresses an action or state change by the same agent, (2) is semantically interpretable without requiring adjacent clauses, and (3) corresponds to temporally coherent segment in motion space. This formulation yields natural intermediate unit between words and full sentences: for example, "A person steps backward, jumps up, runs forward, then runs backward" is segmented into four events, each corresponding one atomic action. We use Gemini 2.5 Flash (Comanici al. 2025 to segment W. The used prompt depicted in Appendix A.9
To interface with motion models, represent each event as event token. Concretely, each clause (or event) C is embedded using the TMR encoder, which we denote by fIMR:
Es fIMR (C*),
Es E IR
where Dy is the embedding dimension of the TMR encoder. Stacking yields the event tokens used by cross-attention.
E RRDD,
= E
E
To complement these event-level representations, we introduce a global text token fTMR (W) derived from the entire prompt W. This token serves holistic summary of the text, allowing
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the model to fall back global semanties when local event cues ambiguous and to maintain coherence across long or compositional sequences.
3.2 ARCHITECTURE OVERVIEW
Overall architecture is shown in Figure Given a textual prompt W and its corresponding motion token sequence M = {m, (m,))=1 where each m, E RR0 and Dm is the pose feature dimension, we train a conditional denoiser ype under a standard forward diffusion process with variance schedule
{3.)
Block overview. We stack N identical Event-T2M blocks. The input to the network is M. In the diffusion process, .M represented the clean motion zo, and at each step we maintain a noisy motion zt. Each block then updates it:
2t - 22 + LIMMM(oncat((rr (I₁,
(1)
ATI G),
(2)
Fe f 0.5 * x2 + (F.(()))
Zt 2, + (onformerSA(ce));
3e - x, + E.A(A, E), St - x4 + ConformerConn(r,) (z+),
+ 0.5 * Ia + FFN(E)),
2t 24 + LIMM(2.).
Text dropout implements classifier-free guidance (CFG) (Ho & Salimans 2022 during training. Empirically, we observed that a 0.5 residual weight yields smoother optimization and improved stability under strong event-level supervision while keeping the feed-forwant contribution balanced with the attention and event-conditioned branches, which aligns with the intuition from Macaron- style architectures (Lu et al. 2019) that split the feed-forward effect across two residual paths.
Local Information Modeling Module (LIMM). We implement the LIMM as a depthwise- pointwise 1D convolutional block, followed by GroupNorm and ReL.U. This design enforces short- horizon smoothness with negligible parameter cost:
LIMM(x1) = ReLU(GN(PW(DW(2)))
where DW denotes depthwise convolution (kernel size 3), PW denotes pointwise convolution, and GN denotes GroupNorm. This reduces reliance on global attention for local kinematics while im- proving stability and contacts.
Adaptive Textual Information Injector (ATII). Unlike conventional cross-attention that mixes text and motion indiscriminately. ATII injects segment-aware semantics through channel-wise gat- ing. Inspired by (Zeng et al., 2025), we first downsample the input motion sequence by a ratio of via a lightweight point-wise convolution layer, yielding M' = (m²) 1.' 1 Then, the global text embedding G is adaptively filtered by the local downsampled motion state m/:
وj Sigmoid Sigmoid(WaImm @ GI) G
(10)
where W. is fully connected projection, e denotes concatenation, is channel-wise product. The gated text feature 'j encodes segment-specific semantics, which are then fused with motion by another projection:
ATTT(,, G) Wy(m's @ فz]
(11)
This adaptive injection mechanism provides stronger alignment between text and local motion, while avoiding excessive overhead compared to full cross-attention.
Conformer for global and local sequencing. ConformerSA() and ConformerConv() correspond to the self-attention and convolutional submodules of a Conformer-style architecture (Gulati et al, 2020). Specifically, ConformerSA() implements multi-head self-attention with relative positional
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bias along time, allowing motion tokens to capture long-range temporal dependencies. In trast, ConformerConv()) applies depthwise separable 1D convolution with Gated Linear Units (GLU) (Dauphin al., 2017), modeling short-range and phase-local motion patterns. These sub- modules play complementary roles: self-attention integrates global context across the motion se- quence, while convolution sharpens local dynamies such step phases or contact transitions. We follow the standard Conformer design, where self-attention and convolution are placed between two feed-forward layers.
Event-based Cross-attention (ECA). To inject event-level semantics, we replace the standard self-attention sublayer each Conformer block with motion-to-text cross-attention mechanism. In this formulation, the motion tokens provide the queries, while the event tokens act as the keys and values.
Let 2²th E L be the current motion context, obtained from the ConformerSA sublayer. For H heads of dimension d,, we compute motion-to-text cross-attention by projecting motion tokens 2! into queries and event tokens E into keys and values:
Qm = xt We E (R())x()))))
(12)
K. = EWW E ((K))(H)))
(13)
Ve EWV (K)
(14)
where splitting across heads gives !!hi K(n) V E R()ds, Multi-head cross-attention is then applied as
(b) = softmax (
(15)
vin
2(h) AA()
(16)
with outputs concatenated as Z = Concat, Z Wo. We then define the event- based cross- attention mapping as
ECA(1, E) = 7 Dropout (2),
(17)
where y is learnable scaling factor initialized near zero for stable optimization.
3.3 DIFFUSION OBJECTIVE AND SAMPLING
We formulate motion generation as conditional denoising diffusion. At each t, a noisy motion sample constructed
E4
Ce To V a.e, e~ 10,
(18)
Then, the denoiser (o is trained to recover z0 from under event-level conditioning:
(()) = Ex.., 1.e I (o G)
(19)
To enable CFG, text conditioning is randomly dropped with probability т, creating an unconditional path during training. At inference, combine conditional and unconditional predictions through CFG, which sharpens motion-text alignment while preserving generative diversity. We adopt 10- step Denoising Diffusion Probabilistic Models (DDPM) (Ho et al. 2020 for efficient generation.
4 EXPERIMENTS
We systematically evaluate Event-T2M to verify whether its event-level conditioning genuinely ex- tends text-to-motion generation beyond single-action prompts. Our experiments combine standard quantitative benchmarks with newly constructed event-stratified test sets and complementary hu- man studies. This design allows us to assess (1) competitiveness on existing benchmark settings, (2) robustness and compositional fidelity on multi-event prompts of increasing complexity, and (3) both the validity of our event-aware decomposition and the perceptual quality generated motions from user's perspective. Together, these evaluations offer comprehensive view of Event-T2MP effectiveness and reveal how explicit event-level representations translate into measurable gains in realism, alignment, and human-perceived naturalness.
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Table 1: Comparison on the HumanML3D, KIT-ML, and Motion-X test sets with existing state-of- are approaches. For each metric, "1" denotes that larger values are better, while "I" denotes that smaller values are better. The best score is marked in bold and the second-best is underlined.
Datasets
Methods
Precision
FID
MM-Dist L
MModality 1
Top-1 1
Top-2 f
Top-3 1
T2M (Gua et
0.455 2013
0.6365.000
0.736 (022
1.087=.02 2221
9.347
2.219
MDM Tevel a PU22
0.320
0. 0.49965000 001
0.611
0.544
5.566
2.79y 072
Motion Ziang
2024a
0.491 J: 200
0.681 (:312:000
0.782
0.630-
3.113- 113--00
1.5534-00
MI.D (Chen et di 602A
0.481
0.673
0.7725
01473=
3. 3.196=-001
2.413#.0m 009
T2M (Zhing
2013
0.491
0.680
0.775 002
0.116-
3.1184-011
1-564-01
ArT2M /hong el Miz-
0.499
0. 1.690--02
0.786
0.112
3.038
2.4524-00:
HumanML3D
FineMaGien Zhung
A023c
0.504
0.690
0.784
0.151
2.908-.J00
2. 2.696t-0m
Graph Dia al 2025
0.504
0.6999.00
0.785
0,116 007
3.070
2.766 000
MMM Playodnustapog
2024
0.515#.802
0.708t
0,804
0
2.926*
1. 1,226+0083
MoMask (Gio Fll 21164
0.521 002
0.713*.00 003
0.807 002
0.045 002
2.908
1.241 040
Light-T2M 20 A026
0.511
0. 1.699-000
0.795
0.040
3. 8.0024-000
1.670
Mo Yaan
0.5291140
0.7191.002
0.8124
W01 0.033
Event-T2M (Ours)
0.5622.00z
0.754
0.842
0.056
2.711
0.944g-.000
T2M et al. 2022
0.361 000
0.55g
0.6815.00
3.022 107
3.488 025
2.052 107
MDM Tovel of KI
0.396
0.497
9. 191 0-191-0003 2-002
1.907
Motion
Zhane
2024g
0.4174.004
0.621 8:001
0.730
1.954
0.730
MLD (Chen et a 21p4
0.390*.006
0. 0.60gtt000
0.734
0.404
3. 3.204
2.192
T2M-GPT
0.40224.00
h.619
8.737
0.717
1.9124-000
ArTTM Zbong el 1144
0.413
0. 1.632
0.751
0.870
3.039
2.281 +-047
KIT-ML
FineMoCicn (Zhing oi di
0.432 000
1.6195.005
0.772
0.178 007
2.869 2.66
187777700
GraphiMetion Uni a 2024
0.429
0. 1.618
D. 0.709
0.313
3.076 002
3.627
MMM Pinyooim Eoprong
2024
0.404
0.621 1.005
0.744
0.316- 112
2.977--013
1,232 000
MoMask (Lio el al 21124
0.433
1.6564-00
0.781
0,204
2. 2.779 002
1.1014.00
Light-22M
2025
0.444
L.77L-007
0,161
2.7464.015
1.0054.000
MoGenTS Yuan el 2024
0.445
0.671
0.797
0,143 1004
2.711
Event-T2M (Ours)
0.439
0.68
0.788
0.159
2.742
0.762
ArTTM Zhong et 2023
0.461 001
0.6644-001
0.768
0.232
3.4554-013
2.0534.003
MoMask (Cin
20144
0.480
0.602
0.768
0.297
3.510=.01
1.4424.04
Motion-X
Light-T2M T2M
Zeng et al. 2025
0.473
6.669.004
0.773
0,1315.012
1.409
1.594t.008
MoGenTS
Yuhn
0.458
0.664
0.768
0.102
3.498 015
0.763
Event-T2M (Ours)
0.519
0.729 001
0.823
0.109
2.979
0.921 10000
4.1 BENCHMARKS AND METRICS
Standard Benchmarks. We adopt the official train, val, and test splits of HumanML3D (Guo
al. 2022), KIT-ML. (Plappert al., 2016), and Motion-X (Lin et al. 2023). Following prior work, motions are represented in root space with root velocities and local joint features. HumanMI.3D provides long and diverse descriptions, KIT-MI. offers shorter prompts, and Motion-X serves as a large-scale dataset, allowing us to assess complex, simpler, and diverse settings.
Event-stratified Subset: HumanML33-EE To examine performance under compositional com- plexity, we apply our L.L.-based event decomposition to H1manMI.3D test prompts and group them by event count: at least 2 events, at least 3 events, and at least 4 events (e.g., "walk left, turn, jump, kick" falls into 24 group). These subsets provide increasingly challenging settings to test whether gains simple prompts transfer to longer, sequential instructions. Full construction details are Appendix A.3
Evaluation Metrics. We follow the standard HumanML3D evaluation pipeline: (1) sample N candidate motions per text (N=20 by default), (2) embed text and motion using the released eval- uators, and (3) compute widely used metrics. Specifically, we report FID (generation realism), R-Precision (text-motion alignment, 1/2-1/ MM-Dist (absolute alignment), and MModality cutra-prompt diversity). Following recent recommendations (Guo et al. 2024 Zeng et al.) 2025), omit the Diversity metric due to instability. All numbers averaged over the test set with 95% confidence intervals estimated from repeated sampling. A detailed definition of each metric is provided in Appendix A.2

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Table 2: Comparison on the HumanML3D, KIT-ML, and Motion-X test sets with MARDM ap- proaches. For each metric, "1" denotes that larger values are better, while "I" denotes that smaller values are better.
R-Precision
Datasets
Methods
FID
MM-Dist
MMModality
CLIP CLiP-score
Top-
Top-2
Top-3
MARDM DDPM Meng et
2024
0.492
0.09g
0.79pa.600
0.116
3.349
2.470=4033
0
Hum
MARDM SIT (Meng et al. 2012
0,5000
0.896
0.796
0.114
3.270
2.231
0,642
Event-T2M (Ours)
0.54
0.744
0.836
0.114
2.948
1.008=002
0,665t-011
MARDM-DDPM Menn stal 2004
0.375 000
0
0.728
0.340
3. 1.48
1.479-07s
0.9811-001 1.901
KIT-ML
MARDM SIT (Meng at 2012
0.387
0.610
0.749
0.242 004
000 3.374
1.312
0.692
Event- T2M (Ours)
0.3774
0.5dg 035
0. 1.722--45
0.2734.013
3.373
0.933+
0.899
MARDM. DDPM Mena et al 2004
0.3922 00
0.502
0.711 404
131324 00
3.844 Dis
2,058 967
0.629 oi1
Metion-X
MARDM (Meng oE 2022
0.405 00
-044
1.000
0.151 mo0
3.761 1.00
1.973 06:
0.648 1.00
Event- Dveat-T2M (Ours)
0.547
0.743
0.834
0.115
2.942 1006
0.963
0.666 Lo00
Table 3: Comparative results on HumanML3344 against ofate-of-the-ar baselines. 2/3/4" denotes prompts with at least 2, 3, and 4 events, respectively.
"Condition
R Precision f
Condition
Methods
FID
MM-Dist
MModality
Top-1
Top-21 f
Top-3
AutTMM Zhong al.
2023
0.47g
0.665 o0s
0.761 5 003
0.171 21771:00
3.1814.010
1,899 1-15
GraphMoluun Unn el al
2023
0.468 0.4684h.000
0.646
0.741#0002
0.252
3.302#.012
2,415
2
Light-T2NT Zeng
MoMask Gud et al 2024
2025
0.497=.00 000
0.4622.003
0.691 ous
0.647 o0s
0.747 008
0.790 oca
0.065 002
0.077 000
3.27811000
3.061=.000 009
1.282 043
1.6921.00
Mo
Yuan al.
2024
0,496
0.690
0,787
0,049
3.039
0,868
Event-T2M (Ours)
0.536
0.732
0.824 002
0.079
2.836 000
0.976
ArT2M Zhong et al.
2023
0.431
0.613 000
0.715 004
0. 0.466 0.31
3.329 018
1. 1.960
GraphMotton et 2023
0.420 000
0.59g 007
006 0.698
0,458 mo6
3.4:0 013
2.427 1.427..00 100
3 MoMask (uuo ct al
2014
0.466
0.6524.006
0.752
0.142
3.169
#.02
Light-T2Mr Zeng e al 4125
0.400
0.504 008
0.699=0002 000
0. 0.193 D02
3.396*.015
1.740=
MoGenTS
Yuan
0.452#0004
0.644 000
0.751 005
0.147 008
3.122 018
0.894 028
Event-T2M (Ours)
0,487#.006
0.687#.004
0.790 004
0.137 00a
2.928=0010
1.010
AtrTM Zhong et al.
0023
0.40718.01
0.581
0.688
4.
3.455
2.049 2.04913.00
GraphMomnn Din el al purs
0.399
0.615#.012
0.723 OL0
0.857#.056 .556
3.521#.040
2.547
MoMask Guo et 2054
0.441#
0.633 014
0,734 013
0.418 500
3.205#004 0412
1.334 846
Light-T2NM Zeng et al. 2025
0.365
0.552
0.662
0.627
3. 3.586
18631.051
MoGenTS Yean el al. 2024
0.420
0.613#.010
0.715
0.423 008
3.241#.009
0.879
Event- Wount-TMMce (Ours)
0.466 10e
0.660 008
0.767 007
0.265 007
3.063
1.039.1.02 1.28
4.2 MAIN RESULTS
Standard test sets (HumanMIL3D, KIT-MI, and Motion-X). On the standard HumanMI.3D. KIT-ML, and Motion-X test splits, Event-T2M achieves performance on par with recent strong baselines (Table 2). This demonstrates that the proposed event-based cross-attention preserves competitiveness on the simple, single-event prompts that dominate existing benchmarks, ensuring that our improvements on complex prompts do not come at the expense of overall accuracy.
Event-stratified sets MLL3an...DD-E), We emphasize that all models are trained on the stan- dard HumanML33D training set and only evaluated on HumanML3DD-E. On these event-stratified subsets, Event-T2M exhibits consistent and substantial improvements, particularly under the most demanding setting of 24 events (Table 3 Figure 2ap. As event count increases, bascline methods that rely on single global text embedding frequently underfit later actions or conflate multiple events, leading to degraded quality (FID) and weaker alignment (R-Precision). In contrast, Event-T2M* explicit eeent-level conditioning enables sequentially faithful synthesis, preserving both action order and smooth transitions.
Efficiency analysis. Figure 2b plots FID at 24 events against the number of trainable parame- ters. Event-T2M occupies favorable point on this curve: it achieves substantially better fidelity under complex prompts while maintaining parameter counts comparable to, or smaller than, recent baselines.
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Light-T2M
ArT2MM
GraphMMtton
MoMask
Event-T2M (Ours)
0.55
ACT
10
fRorome: 275.20M
Top-1 R-Procisiont
FIIX 100
Light-2M
X
4Farams:
FDD
0.8
0.45
0.7
iParams 700 300
08
MoMlssk
R.D-0.00
0.4
IParams: 44,
RE::0.0
Event-T2M (O.m)) AParamsc 9.00M
08
035
x
FID 0.20.27
Al
Al
300 400
Event Count
Event Count
The Number Parameters
(a)
(b)
Figure 2: Overall comparison of Event-T2MM T2M: (a) As event counts increase 22, 24), Event- T2M consistently achieves the lowest FID and the highest R-Precision, while baselines degrade sharply under compositional complexity. (b) Efficiency analysis at 24 events shows that Event- T2M achieves high accuracy with low model size, demonstrating its compactness and scalability.
Table 4: Ablation study on text encoders and conditioning methods on HumanML33-EE
Text Einoderr
Condition
Methods
R-Precision 1
FID
MM-Dist J MModality 1
Top-
Top-21
Top-31
Event level)
0.521 9:00
0.718*
0.815#t002
0.082
2.015
0
Event-T2M (Event-level)
0,536
0.732
0.824
0.079
2.836
0.976 063
Event- T2M (Token-level)
0,463
1.661-0000
0.773
0,162 000
3.031 000
L035 065
TMR
Event-T2M (Event-level)
0.487
0.687
0.790
0.137
2,928
1.110+.00
Event T2M (Token-level)
0.440#t011
0:010
4.740ttc0m
0.306
3.168#
1.14
Event-T2M (Event-lesel)
0.466
0.660
0.7674.000 000
0.265
3.063
1.039: 008
Event- (Token-lovel)
0.474 000
0.664- 000
0.767-
0.153 004
3.1419::01
1875 000
Event-T2M (Event-level)
0.494
0.681
0.779
0.052 1.00
3.079
1.57744.000
Event- T2M (Token-level)
0.423 206
0.618
0.728 1.004
0.206
3,254 R.113
1.9054:006
CLIP
Event-T2M (Event-level)
0.423
0.618
0.729
0.141 t..04
3.245
1.627
Event: T2M (Token-level)
0.399 012
0,597 010
0.709-1000
0.468
3,339
1012
1,991
Event-T2M (Event-level)
0.374
0.57g
0.690
0.425
3,467
1.6741.000
4.3 ABLATIONS AND ANALYSIS
Effect of ECA. Table 4 shows that adding the ECA consistently improves performance on HumanML3D.E. R-Precision rises across conditions, reflecting stronger text-motion alignment, while FID decreases, indicating more coherent and realistic motion. Unlike token-level attention, which disperses semantics across individual words and often fails to preserve ordered dependencies, ECA grounds generation directly in event tokens. This targeted conditioning prevents the model from collapsing sequential actions, cnabling it to respect event order with greater fidelity.
Effect of Text Encoder. Replacing CLIP with TMR yields consistent improvements in R- Precision, particularly on prompts with 23 events. While CLIP's large-scale image-text pretraining captures simple, single-action semantics, it lacks the motion-specific knowledge needed for sequen- tial behaviors. TMR, trained directly on motion-text pairs, provides richer event-centric represen- tations. As result, Event-T2M with TMR only maintains competitive performance on simple cases but achieves clear gains on multi-event prompts, validating our hypothesis that motion-awan encoders are crucial for scaling beyond single-event benchmarks.
4.4
USER STUDY
We conducted two user studies with distinct goals; Full details are in Appendix A.6 and
A.7
Study 1: Validating event decomposition. To test whether our event definition yields a con- action splits, (2)
vincing evaluation basis, compared three alternatives: (1) verb-aware LLM segmentation, and (3) our event-aware LLM segmentation. Verb-aware segmenta- tion simply splits prompts by action verbs (e.g., "run"), without considering temporal coherence
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3
AIT2M
GrapiMation
Mosdank
Lgnn FSM
Ermit-TaM
AUT3M
GrapiMotion
MoMosk
Ligh-T2M TMM
Gr
AIT2M
GrapiMatton
Mosdauk
Ermi-Taa
Eomt
Lien
Figure 3: Results of the user study (7-point Likert scale). Error bars denote standard errors. (a) Fidelity, (b) Order alignment, and (c) Naturalness. Event-T2M achieves significant gains over all competing methods and performs on par with ground-truth (GT).
man walks forward and kicks with foot and walks backward then pauses and then kicks the two times.
Timestep
Event-T2M
(Ours)
omission
omission
addition
AtT2M
omission
omission
GraphMotion
omission
misalignment
Light-T2M
omission
omission
MoMask
Figure 4: Qualitative comparison with a complex multi-event prompt. Event-T2MV executes all events in order and with correct counts, while baselines often fail to generate them faithfully. See supplementary video for full motions.
semantic seff-containment. For each participant, 20 prompts randomly sampled from HumanML3D-E with 23 events, and human evaluators rated whether the resulting segmentation was natural and distinguishable.
The study analysis presents that event-aware segmentation was rated on par with human annotation and significantly better than naive verb-aware segmentation. This validates both the plausibility of our event definition and the reliability of HumanML3D-E as benchmark.
Study 2: Perceptual Validation of Event-T2M4 To examine the perceptual quality motions generated by Event-T2M, compared it with baselines (AtT2M, GraphMotion, Light-T2M, and MoMask) and with ground-truth Human evaluators rated 20 samples from HumanML3D-F along three criteria: (1) how well the motion follows the text without omissions additions (Fidelity), (2) how well the motion follows the order specified in the text (Order alignment), and (3) how natural the motion appears (Naturalness). Ratings were collected 7-point Likert scale.

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As shown in Figure 3) Event-T2M consistently outperformed baselines and achieved scores indistin- guishable from ground demonstrating its strength in faithfully generating multi-even motions.
4.5
QUALITATIVE RESULTS
Figure 4 illustrates model outputs for the challenging prompt "A walks forward and kicks with one foot two times and walks backward then pauses and then kicks into the air two times." which contains seven distinct events. Among the methods, Event-T2M is the only one that realizes all actions in the correct sequence while ensuring smooth transitions. In contrast, baselines often shorten the motion, blend distinct events, or substitute unrelated actions. As further confirmed in additional examples (see supplementary video and Appendix A.8), Event-T2M faithfully maintains both event semantics and temporal order.
FAILURE CASE ANALYSIS
To better understand the limitations existing text-to-motion models in complex scenarios, we conduct a targeted failure case analysis text prompts containing 2 4 events. We specifically focus on long, compositional descriptions that require preserving a chain of distinct sub-actions. Across such prompts, observe consistent pattern: existing models (AttT2M, GraphMotion, Light- T2M, MARDM (Meng et al.) 2024), MoGen'TSS (Yuan et al. 2024), and MoMask) frequently omit events, generate incorrect spurious events, and occasionally produce motions that exceed their maximum motion length or exhibit clear physical artifacts. In particular, MARDM and MoMask often fail to reconstruct the full event sequence, either by dropping intermediate sub-motions or by inserting unintended transitions, suggesting that they struggle to faithfully realize multi-stage, event-rich instructions.
These tendencies are illustrated by the following example: "Man is standing straight, feet not mov- ing, hinges at the waist to reach both hands down his feet, then puts his arms up, bent at the elbows, twists his torso to the left, and then to the right, and then facing forward leans over to the left, and then over the right, stretching." For this prompt, ArT22 fails realize the "hinges at the waist" event, GraphMotion exhibits both event omission and physical errors, Light-T2M and MARDM omit events and sometimes exceed the maximum motion length, and MoGenTS and Mo- Mask also miss parts of the described sequence. By contrast, Event-T2M generates all of the de- scribed events, albeit with a slightly permuted order, indicating that can still cover the complete set of intended sub-actions even when the motion is long and structurally complex.
Overall, these observations suggest that Event- -VM is comparatively more robust at preserving the full set of events in complex, multi-event prompts than prior sentence-level token-level ap- proaches. We attribute this robustness to the event-level formulation, which encourages the model align distinct motion segments with explicit semantic units, thereby reducing the likelihood of dropping, conflating, or corrupting events during generation.
CONCLUSION
We presented Event-T2M, a diffusion framework that leverages event-level decomposition and cross-altention to synthesize sequentially complex motions from natural language. Across Hu- manML3D, KIT-ML, and our event-stratified HumanML3D-E, Event-T2M demonstrates strong performance on standard test sets and consistent improvements under multi-event prompts. Human evaluations further confirm that our model generates motions that preserve both order and semantics while maintaining naturalness comparable to real data.
Our work establishes explicit event-level conditioning as scalable recipe for robust text-to-motion generation, moving the field beyond single-event benchmarks. Nonetheless, challenges remain: current models do not consider long-horizon physical plausibility, natural human-object interac- tions, and seamless integration into downstream applications such as animation pipelines, embodied agents, and video production. Future directions include incorporating physics-aware objectives, abling ane-grained event editing, and extending event- based conditioning to multimodal settings involving vision and audio.
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7 REPRODUCIBILITY STATEMENT
To facilitate reproducibility, provide detailed descriptions of model architecture, training setup, and evaluation protocols the main text and supplementary. In addition, the full implemen- tation and code are included in the supplementary materials, enabling independent researchers to replicate our results.
ACKNOWLEDGEMENT
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (No. 222225-00518444 No. Creative Consilience Program through the Institute of Information & Communications Technology Planning & Evaluation (IIPP) grant funded by the Korea government (MSIT) (IITP-2026-B) 2022-RS-2020--
and by the ICT
1121819).
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A APPENDIX
A.1 IMPLEMENTATION DETAILS
We set the maximum diffusion step to 1000, with linearly increasing variances 3e ranging from 1 x 10-4 to 2 x 10-2 For fast inference, we employ UniPC (Zhao et al. 2023 using 10 time steps. The model architecture consists of N = 4 blocks with a hidden dimension of 256 and downsampling factor of 8. The guidance scale is fixed at 4, while text dropout is applied with probability 0.2. Training is carried out with Adam (Loshchilov & Hutter 2017), using a learning rate of 10" cosine annealing scheduling, and a batch size of 128 two NVIDIA RTX 4090 GPUs. We train for 600 epochs on HumanMM33D and 1,000 epochs on KIT-ML.
During training. checkpoints the lowest FID score on the validation set. In particular, each Event-level Conditioning Module uses local convolution width of 4, and block expansion factor of 2. The Depth-wise Conv ID layers have a kernel size of and a stride of I.
saved at regular intervals, and the final model is selected based on
A.2 EVALUATION METRICS DETAILS
We evaluate our model using several widely adopted measures introduced in T2M (Guo et al. 2022 Below, we briefly describe each metric without relying explicit formulae.
FID assesses how close the generated motions are to real human motions terms of feature dis- tribution. Specifically, it compares the mean and covariance of features extracted from generated samples with those from ground-truth. A smaller value indicates that the distribution of generated motions better matches the real data.
R-Precision measures the semantic consistency between text descriptions and generated motions. For each motion, we form candidate pool of text descriptions that includes the correct caption and several distractors randomly sampled from the dataset. If the true description is found within the top-k retrieved captions when ranking by similarity, the retrieval is counted correct. We report results for k = 2, and 3 to capture different levels of retrieval difficulty.
MM-Dist evaluates the alignment between a generated motion and its paired textual description. computes the average distance between their respective feature embeddings. Lower values indicate stronger semantic correspondence between the motion and its caption.
Multimodalliyy focuses on variation among motions generated from the same text description. For each caption, multiple motion samples are generated, and their feature differences are averaged. A model that achieves higher scores on this metric is better at producing wide range of plausible motions from identical textual input.
A.3 DETAILS ON HUMANML3D AND KIT-ML
The HumanML.3D corpus (Guo al. 2022 is built by integrating motion data from large- scale sources: HumanAct12 (Guo et al. 2020) and AMASS (Mahmood et al.) 2019). These source datasets cover broad spectrum
movements, including everyday behaviors like walk- ing or jumping, athletic activities such as swimming and karate, acrobatic skills like cartwheels, and performance-oriene motions such as dancing.
For consistency, the raw motion clips are standardized to 20 frames per second (FPS). All data are retargeted onto a unified skeleton and aligned so that the character initially faces the positive Z direction. To attach natural language descriptions, annotations were collected via Amazon Me- chanical Turk (AMT). Workers were instructed to write deseriptions, and each motion was labeled independently by almost three different annotators.
In total, HumanML33D provides 14,616 motion clips paired with 44,970 descriptions, using a vocab- ulary of 5,371 distinct tokens. The dataset amounts about 28.6 hours of motion, with clips ranging from 2-10 seconds (average length 7.1s). Captions are on average 12 words long, with a median of 10. To further enrich the dataset, mirroring something or someone with his left leg" was mirrored and relabeled as "A man kicks something or someone with his right leg." ensuring balanced left and right motion coverage.
applied: for instance, the sequence "A man kicks
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Table Sampling Step ablation. represents
Precision.
Table 6: CFG Scale ablation.
Condition
Step
FID
Top-1
R Top-3
Condition
Scale
FID
R Top-1
R Top-3
0.103
0,512
0.054
0.534
0.823
2
0.069
0.529
0.820
0.079
0.536
0.824
10
0.079
0.536
0.824
5
0,092
0.517
0.712
20
0.096
0.530
0.820
0.171
0.800
164
0.471
0.775
3
0.112
0.483
0.788
7
0.138
0,490
0.788
0.137
0.487
0.790
3
10
0.137
0.487
0.790
3
0.186
0.767
20
0,134
0,484
0.792
0.223
0.465
0.769
5
0,280
0,449
0.745
0.335
0.466
0.745
7
0.292
0,460
0.757
0,265
0.466
0.767
4
10 0.265
0,466
0.767
0.368
0.464
0.757
20
0.271
0.461
0.754
6
0,413
0.437
0.718
Table 7: Ablation study on the architecture design (Transformer vs. Conformer).
Condition
Backhone
FID +
Top-1 f
R Top-3 f
Transformer
0.080
0.453
0.736
2
Conformer 0.079
0.536
0.824
Transformer 0.187
0.402
0.700
3
Conformer 0.137
0.487
0.790
Transformer 0.533
0.373
0.670
4
Conformer 0.265
0.466
0.767
The KIT-ML dataset (Plappert et al. 2016) contains 3,911 motion sequences with 6,278 associated textual annotations. The text spans vocabulary of 1,623 words, normalized to ignore capitalization and punctuation. Motions originate from the KIT (Plappert et al. 2016) and CMU (De la Torre et al. 2009) motion capture datasets, but are resampled at 12.5 FPS. Each sequence paired with between one and four textual descriptions, averaging roughly 8 words per sentence.
A.4 FURTHER RESULTS OF ABLATION STUDY
We conducted ablation studies determine the sampling steps and CFG scaling for our model, and the results are summarized in TTbll 5 and 6 respectively. In addition, we examined whether to
adopt a Transformer Conformer within the ECA module, and the results are reported 'Table 7
A.5 DETAILS ON HUMANML3D-E
For reproducibility our HumanML3D-E. we provide the prompts in Table and The LLM used is Gemini 2.5 Flash, which not only demonstrates strong performance but also offers sig- nificantly faster speed compared to other L.LMs. The Human test set contains 4,646 samples, with 2,622 in Condition 2, 927 in Condition 3, and 260 in Condition 4. The visualization of these statistics is shown in Figure 5a
A.6 DETAILS STUDY 1: VALIDATING EVENT DECOMPOSITION
In our user study, we involved 21 participants (11 male, 10 female; = 26.38 years, 4.60, range 22-44). Each participant evaluated 20 samples per condition using a 7-point Likert scale. 1.03), verb-aware prompt (μ 5.07.
The average ratings were: event-aware prompt (μ 6.08,
g = 1.62), and human (μr 6.09, a 1.00). Because the rating data did not satisfy normality assumptions, we adopted non con-aaraettric methods. A Friedman test revealed significant effect of condition (2) = 11.16, < .01). Pairwise Wilcoxon signed-rank tests with Holm adjustment showed that the event-aware prompt and verb-aware prompt differed significantly .01), as
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82
4000
5
3000
2000
2-
1000
0
HumanML3D sets
Event Count Event 3ount Event Count
Evant-aware prompt
Verb-award prompt
Human
(b)
Figure 5: (a) Number of samples in the HumanM test set and HumanML3IDEE. (b) User study of prompts. Error bars denote standard errors. Asterisks denote statistical significance (**; p < 0.01).
the verb-aware prompt and human (p < .01). In contrast, no reliable difference was observed between the event-aware prompt and human (p 0.1546). These findings suggest that the verb- aware prompt condition was consistently rated lower compared to both the event-aware prompt and human, while ratings for the event-aware prompt and human were statistically comparable. Figure summarizs these outcomes.
A.7 DETAILS OF STUDY 2: PERCEPTUAL VALIDATION OF EVENT-T2M
We conducted user study with 20 participants (11 male, 9 female; μ 27.8 years, 6.25, range 22 22-46). Each participant evaluated motion outputs from six conditions conditionsAAtT2MM, GraphMotion, MoMask, Light-T2M, Event-T2M, and ground-truth on a 7-point Likert scale.
Across the three Ceetrics-Fidelle Order consistency, and Vaturalness-clest differences were ob- served. Event-T2M and Ground-truth achieved the highest scores. Event-T2M showed μ 5.29, a = 1.23 (Fidelity), = 5.41, 0 = 1.25 (Order consistency), and μu = 5.03, 0 = 1.40 (Natu- 1.39. μr 5.47, 0 1.38, and
ralness). Ground-truth performed similarly with l 5.45,
5.20, a 1.63. In contrast, the other models remained in the mid-3 range: ArT2M (n1 = 3.57,
1.42). GraphMotion (μ 3.33. o 1.56; μ 3.48, 1.59), Light-T2N. T2M (μ = 3.28, = 1.60; = 3.43, = 1.63; = 3.44, 1.49), and MoMask (μ+ = 3.48, = 1.52; Ja 3.61, a 1.58; μ 3.77, a 1.554. These results indicate that Event-T2M and Ground-truth clearly outperformed the other methods across all evaluation dimensions.
1.43; μ 3.64, 1.47: μ 3.84,
1.62; 3.67,
Since normality assumptions were not met, employed non-parametric tests. Friedman tests vealed significant effects condition all three criteria (Fidelity: x x²( 53.887, $7.p1 < .01; Order alignment: (5) 51.82, p .01; Naturalness: 25) 47.85, < .01). Pairwise Wilcoxon signed-rant tests with Holm correction further indicated that Event-T2M T2M consistently outperformed all competing models (p < .01 across comparisons). In contrast, comparisons between Event- T2M and ground-truth did not yield reliable differences (Fidelity: p = .0890; Order alignment:
p 2905;; Naturalness: p .785)..
These results suggest that Event-T2M achieves ratings comparable to ground truth while signifi- cantly surpassing existing baselines across all evaluation aspects.
A.8 FURTHER VISUALIZATION
Figure compares generations for the prompt "A person steps backward, jumps up, runs forward, then runs backward." Wvent-T2M alone faithfully executes all four actions in the correct order, with smooth transitions. In contrast, baselines either truncate the sequence, merge events, or substitute incorrect motions.
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person steps backward, jumps up, runs forward, then runs backward.
Timestep
Event-T2M (Ours)
8ddition
AttT2M
omission
wrong direction
wrong direction
GraphMotion
omission
wrong directton
Light-T2M
wrong event
addition
MoMask
Figure 6: Further qualitative comparison.
A.9 RESULTS OF DECOMPOSITION BY LLM PROMPTS
The quality of event decomposition is highly dependent on the instructions provided to the LLM. To investigate this, we designed different prompting strategies, as shown Table and
Quantitative results in Figure show that the event-aware prompt produces more reliable and con- sistent decompositions. When using event-aware decomposition, Event-T2M achieves higher R- Precision and lower FID, particularly in the 23 and 24 event subsets. By contrast, the verb-aware prompt often over-segments or under-segments into two independent actions, merging "run, stop, and jump" into one), which introduces noise in the event tokens and weakens the conditioning signal. This results in degraded alignment between generated motions and text, as reflected in both retrieval-basee and distributional metrics.
text (e.g., splitting "walk forward while waving"
These findings confirm that a carefully designed event-aware prompt is crucial for leveraging LLMs in motion-text decomposition. Rather than naively extracting all actions, grounding the segmenta- tion process in a principled definition of events yields more stable event tokens and stronger down- stream performance.
A.10 LLM USAGE STATEMENT
In preparing this paper, we used an LLM solely as a writing assistance tool for grammar correction and minor language polishing. The LLM was not involved in research ideation, methodology design, data analysis, or result interpretation. All scientific content, experiments, and conclusions were fully conceived and verified by the authors.
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"a person steps forward, sits down, taps feet together while rubbing hands together."
Event-aware
Verb- aware
-a person steps forward."
-a person steps forward."
"a person sits down."
"a person sits down."
"a person taps feet together while rubbing hands together."
"a person taps feet together."
-a person rubs hands together."
"a standing person is swaying gently back and forth he holds his left hand the left side his head moment. he drops his arm, briefly, then raises it to look at his hand."
Event-aware
Verb-aware
"a standing person is swaying gently back and forth holds his left hand to the side of his head for moment."
-a standing person is swaying gently back and forth."
"a standing person holds his left hand
"a standing person drops his arm, briefly."
to the left side of his head for moment."
"a standing person then raises it to look at his hand."
"a standing person drops his arm, briefly."
"a standing person then raises it look his hand."
"a person kicks with their right leg twice, and then once with their left."
Event-aware
Verb-aware
"a person kicks with their right leg." person kicks with their right leg." "a person kicks once with their left."
"aa person kicks with their right leg twice." person then kicks once with their left."
"a person making rapid swinging motions with their right leg in the air, while holding onto something with their right hand."
Event-aware
Verb-aware
"a person is making rapid swinging motions with their right leg in the air, while holding onto something with their right hand."
"a person making rapid swinging motions
with their right leg in the air."
"a person holding onto something
with their right hand."
"a person dodges things thrown at them by blocking with their left hand and then ducking."
Event-aware
Verb-awe Verb-aware
"aa person dodges things thrown at them by blocking with their left hand." "a person then ducks."
person dodges things thrown them." -"a person blocks with their left hand." "a person then ducks."
Figure 7: Overall comparison of Event-T2M.
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Table 8: Event-aware prompt: Incorporates our proposed definition of event guide segmentation.
Event-aware Prompt
Please segment a single input sentence into multiple sentences that each represent dis- tinct event, following the rules below:
A bundle of actions performed simultaneously at specific point in time is defined as a single "event".
Each segmented sentence must start with the subject used in the original sentence (e.g., "a person", "a man", etc.).
Do not remove or simplify any adverbs, adjectives, or modifiers that appear in the original sentence - preserve them as much as possible.
Parts separated by the # symbol must be included in each segmented sentence.
Do not add any new sentences only break down the given text instructed.
Do not include your thinking process or output reasoning, Only output the segmented sentences following the format.
If the sentence cannot be further segmented into multiple events, leave it is and output the original sentence without any changes.
Even there are grammatical errors in the sentence, proceed with the processing.
the input sentence contains multiple actions, the output must contain the same num- ber of actions as the input sentence.
Good Example Input
man lifts something on his left and places it down on his right. #a/DET man/NOUN lift/VERB something/PRON on/ADP his/DET left/NOUN and/CCONJ place/VERB it/ROON down/ADP on/ADP his/DET righ/NOUNHO..HO.00
Good Example Output
man lifts something on his left= man/NOUN lift/VERB something/PRON on/ADP his/DET left/NOUN*OOOHHI man places it down on his #aDET man/NOUN place/VERB it/PRON down/ADP on/ADP his/DET righ//NONNHO.HHO..
Good Example Input
man kicks something with his left lcg.#a/DET man/NOUN kick/VERB some- thing/PRON with/ADP his/DET left/ADJ leg/NOUNHO.0HO.0
Good Example Output
man kicks something with his left leg.#a/DET man/NOUN kick/VERB some- thing/PRON with/ADP his/DET left/ADJ leg/NOUNNO.OHHO.0
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Good Example Input
person waves their hand while stepping sideways, then jumps up and spins, and finally lands and bows. #A/DET person/NOUN wave/VERB their/DET hand/NOUN while/SCONJ step/VERB sideways/ADV then/ADV jump/VERB up/ADV and/CCONI spin/VERB and/CCONJ finally/ADV land/VERB and/CCONJ bow/VEDDO0000.00
Good Example Output
A person waves their hand while stepping sidDE person/NOUN wave/VERB their/DET hand/NOUN while/SCONI step/VERB sideways/ADVHHO.Rt person jumps up and spins.#A/DET person/NOUN jump/VERB up/ADV and/CCONJ spin/VERB#O.OHO..
person lands and bAWDE person/NOUN bow/VERB#0.000000
land/VERB and/ CCONJ
Bad Example Input
somcone is sprinting side to side#someenc/PRRO to/PART side/VERB#O.OOO..0
is/UUX sprint/VERB
side/NOUN
Bad Example 1 Output
The input sentence cannot be segmented as requested because "side to side" is single adver- bial phrase modifying the verb "sprinting," " and "side" is used as noun and a prepositional phrase. The sentence only contains one action (sprinting).
Bad Example Input
A person claps their hands while sitting the ground. #A/DET person/NOUN clap/VERB their/DET hands/NOUN while/SCONi sit/VERB on/ADP the/DET ground/NOUNHOO.OOO
Bad Example Output
A person claps their hands. #A/DET person/NOUN clap/VERB their/DET hands/NOUN#O.Omm 1000000000 A person sits on the ground. #A/DET person/NOUN sit/VERB
on/ADP the/DET ground/NOUNHO..HOO.
Bad Example Input
A person bends their knees and raises both arms the time. #A/DET person/NOUN bend/VERB their/DET knees/NOUN and/CCONJ raise/VERB both/DET arms/NOUN
at/ADP the/DET same/ADJ tine/NOUNHO.0H..0
Bad Example Output
person bends knees/NOUNHO..00
A
their
knees.#A/DEF
person/NOUN
bend/VERB
their/DET
A
person raises both
#rms.#A/DET person/NOUN
raise/VERB
both/DET
arms/NOUNHO.OHO.0
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Table Verb-aware prompt: Segments sentences based only on individual actions without an event- level definition.
Verb-aware Prompt
Please segment a single input sentence into multiple sentences that each represent a dis- tinet event, following the rules below:
Each segmented sentence must start with the subject used in the original sentence (e.g., "a person", "a man", etc...
Do not remove or simplify any adverbs, adjectives, or modifiers that appear the original sentence preserve them much possible.
Parts separated by the # symbol must be included in each segmented sentence.
Do not add any new sentences - only break down the given text as instructed.
Do not include your thinking process or output reasoning. Only output the segmented sentences following the format.
Even if there grammatical emror in the sentence, proceed with the processing.
If the input sentence contains multiple actions, the output must contain same number of actions as the input sentence.
Good Example
Input
lifts something his left and places it down his right.#//EET man/NOUN lift/VERB something/PRON on/ADP his/DET left/NOUN and/CCONJ place/VERB it/PRON down/ADP on/ADP his/DET righ//NOUNHO.HH..0
Good Example
Output
a man lifts something on his left.#a/EET man/NOUN lift/VERB something/PRON on/ADP his/DET let//NOUNO..HOO. man places it down on his right#a/DET man/NOUN place/VERB it/PRON down/ADP on/ADP his/DET righ//OUNNHO.OOO.0
Good Example Input
a man kicks something with his left leg.#a/DETT man/NOUN kick/VERB some- thing/PRON with/ADP his/DET left/ADJ ley.UUHH.OOO
Good Example Output
a man kicks something with his left leg.#a/DET man/NOUN kick/VERB some- thing/PRON with/ADP his/DET IO/HUADOOO//
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Good Example Input
man picks up a heavy box and carries across the rooD.#A/DET man/NOUN pick/VERB up/ADP a/DET heavy/ADJ box/NOUN and/CCONJ carry/VERB it/PRON across/ADP the/DET room/NOUN40.0H..0
Good Example
Output
A man picks up heavy boX.#A/DET man/NOUN pick/VERB up/ADP a/EET heavy/ADJ box/NOUNHO.Co 0#0.0 A man carries it across the room.#A/DET man/NOUN carry/VERB
it/PRON across/ADP the/DET room/NOUNHO.0H0.0
Bad Example 1 Input
is sprinting side to side#someonc/PRON is/AUX sprint/VERB side/NOUN to/PART side/VERB+O.0H0.00
somcone
Bad Example - Output
The input sentence cannot be segmented requested because "side to side" is single adver- bial phrase modifying the verb "sprinting." " and "side" is used as a noun and a prepositional phrase. The sentence only contains one action (sprinting).
Bad Example Input
A person jumps high and lands so/tIY person/NOUN jump/VERB high/ADV and/CCONJ land/VERB softly/ADVOO.OHO.0
Bad Example Output
A person jumps high and lands 4A/DEY person/NOUN jump/VERB high/ADV and/CCONJ land/VERB softly/ADVHO.OHO.0
Bad Example Input
man runs quickly and turns #a/t. man/NOUN run/VERB quickly/ADV and/CCONI turn/VERB I0000/ADVO.00000
Bad Example Output
runs #DDET man/NOUN run/VERB quickly/ADV40..HH..0
A.11 VALIDATING THE ACCURACY OF L.M-GENERATED EVENT LABELS.
To directly measure the reliability of the LLM-based event decomposition, we perform human eval- uation on 300 randomly sampled HumanML33-E prompts. Three trained annotators independently assess whether each LLM -egerreedt segment (1) is grammatically well-formed and (2) matches our event definition when both criteria are satisfied. Under this protocol, the LLM achieves a 93.3% correctness rate, indicating that the event splits sufficiently accurate for benchmark construction.
minimal, semantically self-contained action. A segmentation is accepted only
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Table 10: Comparative results on KIT-ML-E and Motion-X-E X-E against baselines. "Condition 2/3/4" denotes prompts with at least 2, 3, and 4 events, respectively.
Datnsets
Condition
Methods
R- R-Precision
FID L
MM-Dist
MModdaiit 1
Top-1
Top-2 T
Top-3
AUT2M
Zhong al 2025
0.320 o11
0.51g 010
0.610=0012
012
0.636 067
3.508 016
2,097
MoMask Up0 et al. 2023
0.393
0.556
0.708 0.708--000
0 0.380-4026
3.0544
1.268 1.268.-03
Light- T2M
0.417
0.614
0.734
0. 0.392--018
2.907 2.22
1.3377-000
Mo
Jiih ci sl 20,4
0.353
0.581
0.721 0.7214440
0,424 075
3. 3.100-A000
0.720
Event-T2M (Ours)
0
0.575
0.707
0
3.020 028
0.794
Atr22M Zhons et al 2025
0:20y=
0.405=.010
1. 1.765* 174
3.524 954
2.053 052
MoMask
100 mr 6124
0.2424-012
0.374
0.476
0.981
3. 3.126
1 1.201
KIT-ML-E E
Light-12M
MaGinTS
Taan m
0.207
0.197
0.448
0.346
0.470
0.554
0.855 0
0.70
3.058 054
2.785 7.78
0.712
1.265
Event-T2M (Ours)
0.241
0.406
0.520
0.678 020
2.672
0.769 023
Zhong et
0.316 032
0:52g5 002
0.088 001
9.190
3,956
2.527 131
MoMask 155 et nl. [044
0.434
0.683
0.713
4 4.565
3.801
1 1.292
Light T2M
0.416
0.653
0.734
3.639 007
108 3.159
1.568 072
MoGenTS
Yuan al
0.33844016
0.5074.00
0
3.894
3,387
0. 0.866-
Event-T2M (Ours)
0.550
0.641
0.716
3 276
3,661 1 072
0
AUT2M Zhone et al 2025
D-125
0.628
0.74
0. 0.350=t002 021
3.728
2.289= 052
MoMask paa mi sina
0.42g
0.633
0.746
0. 0.362
3 3.698
1 1.4924
Light- T2M
0.441
1.613
0.719-0003
0.17
3.674
165572005
Mo
man ml 2024
0:432
0.645
0, 0,7571.004
0.116 000
3 6013
0 0.835 0.835-0000
Event-T2M (Ours)
0,524 10006
0.728
0,825
0,111 1 003
2.984 003
0,902 018
Atr12M Zhong et al. 2025
0.3477 000
0:541
0.760 012
4.071 025
2.512 070
MoMask 1000 mr. 6144
0.159
0.559m.00s
0.68 0.681
0.695
3.8672.020
1 1.620
Motion-X-E X-E
Light-12Mr
0.369..00
0.672 0.308=.00
0. 0.364 021
3:901 0000=0000 010
1.736++009
MaGenTS
Yaan m
0.366
0.507
0.672 006
0. 0.169
3. 3.806
0.843 026
Event-T2M (Ours)
0
0.729 1005
0.825
0
2,981 1 025
0.9-9
AuT2M Zhong al 2025
0.311 010
0.193= 014
0
1. 156 l31
4.224 076
2.782 078
MoMask U30 el al. 2024
0.300
0.50g
1.224 0.625--01
0.625
4 4.066-.007
1.770.1.06
Light
cl
0.310 012
0.195 011
0.616=.01a
1.08g 064
1224 000
1915#+000
MoGenTS
Yuan al. A124
0.323
0.516-0012
002
0.6291.0m
0.7444 054
4.1184
0.9534
Event-T2M (Ours)
0.362
0.567
0.697
0.43
3,959
0
A. EVALUATION ON MORE DATASETS
We constructed event-level benchmarks for KIT-MI. and Motion-X and evaluated all baseline mod- els under identical conditions (Table 10 and 11.. Because KIT-MI. consists largely of simpler motions, the number of samples containing > 4 events is extremely limited; thus, we used a batch size of 16 for evaluation under that specific condition. Across both benchmarks, Event-T2M demon- strated consistent and competitive performance, indicating that its effectiveness is not restricted to any particular dataset or benchmark setting.
A.13 COMPARISON WITH RETRIEVAL- MOTION SYNTHESIS
We additionally compare Event-T2M with representative retrieval-augmented baseline, ReMoD- iffuse (Zhang et al. 20236), which improves motion quality by retrieving motion conditioned on the input text and refining them with a diffusion model. Quantitative results on HumanML33-E are reported in Table 12 Across all conditions, Event-T2M achieves better text-motion alignment and lower FID than MeMouuse and the margin becomes larger as the number of events in the prompt increases (Conditions 2, 3, and 4).
We attribute this trend to a fundamental limitation of actrieval-based pipelines under compositional complexity. ReMoDiffuse relies on matching the prompt to existing motions, which is effective when the description corresponds to a single, relatively simple action, but does not provide an ex- plicit mechanism to align multiple ordered semantic units to distinct temporal segments. As a result, for long multi-event prompts, the retrieved motions often cover only part of the description or fail to respect the full temporal structure.
In contrast, Event-T2M explicitly introduces event boundaries in the text domain and conditions the denoising process event tokens that are intended to control temporally extended motion segments. This event-level conditioning allows the model to synthesize novel combinations of sub-actions rather than relying solely on recombining existing trajectories. Empirically, this design leads to
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Table 11: Comparative results on HumanML3D-E, KIT-ML-E and Motion-X-E against MARDM baselines. "Condition 2/3/4" denotes prompts with at least 2, 3, and 4 events, respectively.
Dataeets
Conditon
Mothods
R-Pratsion
MM--iii
MModaliy
CLIP-scure
Tep-1
Tep-27
Tep-37
MARDM-DDPM Menyet al 2004
04444-905
0.6584-954
0,7021-013
0.1571.8
8.465--055
2331 om
DC.
MARDM-SIT [Mergeeaa (124
0.4791-914
0.771
0,191
3.00g--011
2.296- 000
Event-T2M (Ours)
0.536 011
0.782 00
0.116 077
3 196
1.044 10
0.663
MARDM DDPM Menzet al 2005
0.433
0.621
4-824
0,731
0.301
3.500*000
2
0.616
HumanML55D
MARDM-SIT Manges
0.44 on
0.652 015
0
911
0 104
8.54 m1
2
0.625
Event-T2M (Ours)
0,508 -011
0,708
0,800 1.11
0.131 4-09
3,045 -18
1.082
0,66 1BL
MARDM-DOPM Menretal al 2014
0.397
0.5855
0.088
0.61g
3.0073.003
2
0613
MARDM-SIT (Mng ai 2024
0:4284-50
0.608+-911
0,7074-813
0.71g-454
3.070++430
2,506-
0.21
Event-T2M (Ours)
0.4631-93
0.6631-977
0,781
0.259
3,281 1.015
1,137=
0.69
MARDM-DDPM Mona et all 2004
0.39)
0,578
0
0.670
8.729
2,494
0661
MARDM-SIT (Melig p029
0,3324-500
0,5461-93
0.689--10
0.442
0,535+4/033
1.595-
0.674
Exent-T2M (Ours
0,346
0.508+-97
0.821
0.62
3,436 100
0
0.67
MARDM-DDFM Moneet 2004
0.5154-823
0,4484-455
5.647--22
3.1674.16
2.293
0.598-100
XIT-ME-E
MARDM-SIT [Merg ei pM04
0,209 0,205
0,35944444
0,03-4-413
1,5
3
3.5014-00
1
0.650
Event-T2M (Ours)
0.241
0.406
0,536
1.477
3,05
1.122
0L681
MARDM DDPM Monnet 2204
0.341 0.041
0.578
10.3754955
4.880+110
2,03
0.567
MARDM SIT [Meg er 2024
0.359
0.584
0,700
7.045
4.819
2.285
05155
Event-T2M (Our))
0.346
3.733
MARDM-DOPM MMnxett 2014
0.3
0,5831-91
0,2032.0
01181-000
3.008 3.908--014
2.189
MARDM-SIT (Mngge
0.3574-914
0.508
0.716+-911
3.51++-
2.1
0633--46
Event-T2M (Ours)
912
6.7351-912
0.112
3,009
1.047
0.664 FEC
MARDM- DDPM Mencet 2005
0,000 or
0:300 Fea
Metion-X-E
MARDM-SIT MMng ar 2024)
0.356 ois
0.531
0.200 107
3.727
2
0.617
Exent-T2M (Ours)
1-906
0.805--903
3 co0
0.663 DEL
MARDM-DOPM Menret al 2004
0.322 115
0:53g4-011
0.500 a1a
0,925 179
3,672 100
2.154 mx
0.596
MARDM-SIT Mengrra
0.3404-644
0.656 44255
0.087
0.972--008
2.150 Aeo
-775
Event-T2M (Ours)
0.452 077
0.600 970
0.776 007
0.250 199
3,29 01F
L,005 o00
0.659
Table 12: Comparative results HumanML3D-E against retrieval-bast baselines. "Condition 2/3/4" denotes prompts with at least 2, 3, and 4 events, respectively.
Condition
Methods
R- R-Precision 1
FID J
MM.Dist
J
MModality 1
Top-1 f
Top-2
Top-3 f
ReMoDiffuse Zhong et al.
20236
0.6574 L.47
0. 0.755
0.151
3. 1.722
2.937
2.
Event-T2M (Ours)
0.536
0.732
2002
1.824
0.079 Lo00
2.836
0.9761.043
ReMoDiffuse Zhang et
202th
0.444
0.630 A01
0. 0.70
0.29
3.144
2.851 8.087
3
Event-T2M (Ours)
0.487
0.687
0.790
0.137=0005
2.928
1.010
ReMoDiffuse Zhung et al. 2023b
0.392#.011
0.572
0.674#.000
0.583+.000
3.24
3.106
4
Event-T2M (Ours)
0.466 Lo00
0.660 cos
0.767 1.167
0.268 L.007
3.063
1.015
1.039
better preservation of event ordering and fewer omissions on HumanML3D-E, particularly in the high-complexity regime where retrieval-based approaches struggle.
A.14 VERB-AWARE vs. EVENT-AWARE CONDITIONING
To directly assess whether event-aware conditioning offers advantages over verb-aware or hybrid (verb + event) formulations, we conducted additional experiments using the verb-aware prompts introduced in Table of the supplementary material. Based on these prompts, we re-preprocessed HumanML3D-E and retrained (i) verb-aware variant that conditions solely on verb-level units, (ii) a hybrid variant that combines verb-level units with global text features, and (iii) our event-aware model built from the event-aware prompts in Table 8. Quantitative results across different event- count subsets are reported in Table 13
As summarized in Table 13, the event-aware model consistently achieves higher R-Precision and exhibits more stable FID than both the verb-aware and hybrid variants across all complexity lev- els. Verb-aware conditioning treats individual verbs as the primary semantic units, which captures instantaneous actions but does not encode how they unfold as temporally coherent segments. In contrast, event-aware conditioning operates clauses that bundle the action together with its guments, affected body parts, and temporal scope, providing a more suitable alignment target for motion segments.
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Table 13: Comparative results on HumanML3D-E against verb-aware and event-aware conditioning. "Condition 2/3/4" denotes prompts with at least 2, 3, and 4 events, respectively.
R- R-Precision
Condition
Methods
FID 4
MMM-Dsst..
MModality
Top-1 ?
Top-2 f
Top-3
baseline
Verb-aware conditioning
0.518*+008 u0e
0.738 00
0.830
0.085
004
2.772=
1.1644.02
Event-aware conditioning
0.562
0.754
0.842#-002
0.056 002
2.711
0.9:9*-020
Verb-aware conditioning
0.518
0.712 ood
0.810$.002 002
0.128
000
2.913 006
1.309=t000
2
Event-aware conditioning
0.536 000
0.732 0012
0.824 002
0.079 ond
2.836 006
0.9764.04z 040
Verb-aware conditioning
0 0.479
0.681 #
0. 5.000
0.193 0006
2.900#.011
1.292
3
Event-aware conditioning
0.487#.000
0.687 000
0.790
0.137 000
2.928=0010
1.010#.029
Verb-aware conditioning
0, 46 1009
0.65.1 .010
0.7.18 008
0.317# 011
3.102 (120
4.064 0044
Event-aware conditioning
0.466 0.46650000 #0008
0.660
0.767#.007
0.265
3.063#-015
1.039
Table 14: Comparative results on HumanML3D-E (2 events) with human-annotated, LLM-free
test set.
R-Precision
Annotator
Methods
FID 4
MM-Dist 1
MModality
Top-1 f
Top-21
Top-3 f
AuT2M Zhong et al.
2025
0.410
0.584+.015
0.687+.010
1.0544.004
3.464=004
1.273#.500
Human
MoMask (uuo of al.
2024
0.14
0.631
0.733*.0m
0.4
3,205 041
1.3374.00 040
Event-T2M (Ours)
t.05
0.650
0.762#.006
0.297
3,036 022
1.040#.029
AuT2M Zhong al
2023
0.408+.019
0.595+.024
0.699*+029
L.524.109
3.495
000
1.656*.918
Human 2
MoMask Guo 0241
0.435
0.625
0.729:5002
0.420
3,238= 010
1.349
Event-T2M (Ours)
0.457
0.6674.010
0.766*.009
0.281
0.281 #.007
3.044=022
1.061 #.031
AuT2M Zhong et al]
2023
0.393
0.674
0.6794.014
1.0784.087
U87
3,513=0057
057
1.431 354
Human 3
MoMask Guo al.
2024
0.441
0. 2.63
0.746
0.4371.029
3.187= /06
1.389 co0
Event-122 (Ours)
0.494 0007
0.685
#.00
0.276
3.005= 017
1.04 040
AtT2M Zhong al.
2023
0. 0.407 013
0.581
0. 68688..00
L.074.104 104
3. 3.455=001 on
2.049 con
LLM
MoMask (Guo al.
2024
0,4118
0.63
0.73g4.015
3,205 012
1.334
Event-T2M (Ours)
0.466 008
0.660 Das
0.767 007
0.265
3.063=00
1.039
These differences become most pronounced in the 4-event setting, where long-range sequential de- pendencies are strongest. In this regime, the verb-aware model frequently merges or reorders sub- actions, and the hybrid model only partially alleviates these issues by mixing verb-level information with global context, but still lacks explicit temporal boundaries. The event-aware model, on the other hand, benefits from conditioning on temporally extended event tokens that map more directly contiguous motion segments, which leads to better preservation of event ordering and fewer omis- sions on complex multi-event prompts. Overall, these findings suggest that the gains of event-aware conditioning come not from additional supervision, but from providing the diffusion model with semantically and temporally grounded units that better match the structure of human motion.
A.15 EVALUATION ON A HUMAN- ANNOTATA, LLM-FREE EVENT TEST SET
To test Event-T2M independently of the LLM-based event decomposition, we constructed an ad- ditional complex motion test subset with fully human-segmented events. Three trained annotators manually segmented all prompts with 4 events according to our event definition. For these long and structured descriptions, annotators produced highly consistent segmentations, and the resulting subsets substantially overlapped with the prompts selected by the L.M.. We then evaluated Event- T2M and all baselines on each of the three juman-segmented splits. As reported in Table 14, the performance trends closely match those observed on the original LLM-based HumanML3D-E split: Event-T2M maintains clear advantage over all baselines across annotators in terms of both text-motion alignment and FID.
To directly address the concern that the baselines' poorer performance might be an artifact of the LLM pipeline, we further analyzed AuTMM and MoMask on the same three annotttdd sub- sets. For each method, we compare results on (i) the original L--e-based split and (ii) the three
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Table 15: Comparative results on HumanMM33-E against baselines with TMR encoder setting. "Condition 2/3/4" denotes prompts with at least 2, 3, and 4 events, respectively.
R-Precision 1
Condition
Mothods
FID
MM-Dist
MModality
Top-1 1
Top-21 1
Top-3 1
AuT2M
Thong et
2023
0.518
0.7075.005
0.799:..0m
2.146
2.987
1.594
MuMask
Guo all
2014
0.4874-0m
0.684=000
0,783
0,284
3.116-4-00
1.158-
baseline
MoMask
Grn nl.
2014
(k/v)
0.49
0.683
0.781
0.240
3.113
1.2144.045
Light-T22N
[eng et 2025
0.554
0.746=-002
0.836=0022
002
0.053
2.750
0.970= 040
Event-T2M, (Ours)
0.562#.002
0.754 003
0.842 002
0.056
2.711
0.949= 025
AIT2M Zhong et al.
2023
0,500)+-014
0.681
0
0.199 0.
3.089
1,688
MoMask Guo et al.
20M
0.470-1.00
0.665
0.665-.002
0.769
0.769
000
0.385 1.om
3.194
1.174
2
MoMask
Guo
2084
(k/v)
0.479
0.668
0,766
0,291
3.196
1.217#004
Light-T24
[Zong et al. 2025
0.527
0.722
0.815=0002
0.087##000
2.885
0.984
Event-T2M (Ours)
0.536
0.732 002
0,824 002
0.079 000
2.836
0.976 043
AIT2M
Zhong
2023
0.445
0.631
0.732
0.474
3.251
1.798-.117 111
MoMask Guo al.
2024
0.430
0.622
0,733
0,544
3.289
1.190 1.190
3
MoMask
Gao al
2024
(k/v)
0,445#.004
0.629=.005
0.731
0.364
3.29778.020
1.228 045
Light-T2M4
[Zog
2025
0.487#.006
0.680 005
0 000
0.139 000
2.987##.030
1.005 033
Event-T2M. (Ours)
0.4874.006
0,687
0,790
0,137
2.928
1.010
AuT2M Zhong et al. 2023
0.424#.017
0.598=.025
0,701
0,789##004
3,335++.107
1.647=-409
MoMask Guo et al. 2024
0,417
0.601 2.005
0,708
0.821
3.392
1,230 1.230=.040
4 MoMask
Guo
2024
(k/v)
0.413#.0011
0.590=
0,703=0011
0.682 .04
3.479
1.251 1.251=0051 +.051
Light-T2M [Zong ot al. 2025
0.43644000
0.609 o0s
0
0,361 4.000
3.31944017
Event- (Ours)
0.466
0.660 005
0.767
007
0.265
1.00r
3.063
1.03920026 026
human semaan-eegmente splits TTalle 14). Across all metrics, the differences between the LLM--asee and human-annotated results are very small. For Att22M, Top-k R- R-Precision and FID on the human- segmented splits remain within narrow margin of the LLM-based scores, without any consistent upward shift. MoMask exhibits the same pattern: Top-k retrieval, FID, MM-Dist, and multimodal- ity on the human-annotatee annotated subsets fluctuate only slightly around the 4LM-based values, sometimes marginally higher and sometimes marginally lower.
These observations do not support the hypothesis that the baselines' weaker performance on H3manMM33D-E is caused by a mismatch with the LLMM-based segmentation pipeline. Instead, they indicate that AttT2M and MoMask behave similarly on both LLM-segmented and complex prompts, while Event- -ee retains a consistent advantage in all cases (Table 14p.
A.16 EVALUATION IN MODEL CONFIGURATION EMPLOYING THE TMR ENCODER
To ensure a fair comparison, we conducted additional experiments by replacing the CLIP text en- coder with TMR in baseline models. Especially, we conducted experiment MoMask that uses TMR's word-level tokens as key/value in cross-attention (indicated as MoMask (k/v)).
Table 15 shows that replacing CLIP with TMR does not act as uniformly strong boost across all baselines. For AtrT2M, using TMR tends to slightly improve text-motion alignment metrics, but at the same time leads less favorable behavior in terms of distributional quality (FID) and motion diversity (MModality). MoMask is an even more extreme case: with CLIP it is very strong on distributional metrics, but when we drop in TMR with the same architecture, both alignment and FID move in a clearly worse direction. In contrast, Light-T2M, benefits more consistently from TMR: alignment improves across the board and, especially on subsets with larger number of events, FID also becomes better. In other words, we observe a heterogeneous pattern where gains and losses coexist and depend strongly on the underlying architecture.
The MoMask (k/v) experiment was designed to test whether injecting TMR word-level tokens more directly as key/value would change this picture. We find that in some conditions this variant outper- forms the plain TMR version of MoMask in terms of alignment or FID, but compared to CLIP-based MoMask the overall distributional quality is still worse, and on the most event-complex subsets the performance gap is not fully closed. Thus, neither switching the encoder to TMR nor directly feeding TMR tokens into K/V makes MoMask adopt TMR as a clearly and consistently superior configuration over CLIP on HumanMI13D-FF.
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Table 16: Ablations for LIMM and ATII. "Condition 2/3/4" denotes prompts with least 2, 3, and 4 events, respectively.
R- R-Precisson
Condition
Methods
FID 4
MM-Dist +
MModality 1
Top-1
Top-2 f
Top-31
w/o LIMM, ATII
0.514 003
0.703+..02
0.797=.002
0.302 006
3.104=.002
1.386 1.386+.008 4.68
w/o LIMM
0.535 002
0.730 003
0.824 002
0.255 005
2.841=
=000
1.035--002
bascline
w/o ATII
0.519 002
0.709 001
0.802=0001 001
0.052 002
2.945
-94550006 000
1.255:t-088
Event-T2hh (Ours)
0.562
0.754 003
002 0.842
0.056 002
2.711 000
0.949 200
w/o LIMM, ATII
0.498 002
0.693 002
0.792 m. 008
0.347 007
3.263
106330008
1.555
w/o LIMM
0.519 .033
0.716+t.002
0.814=0002
0.227
003
2,908=.000
10564.087
2
w/o ATII
0.490 003
0.676 002
0.772 002
0.076 003
3.098
008
1.38522-43
Event- T2M (Ours)
0.536 - .022
0.732 002
0,824 002
0.079 003
2.836
006
0,976:t.043
w/o LIMM, ATII
0,445=000
0.644#t000 003
0.754#.004
0.536 012
3,453=.011
011
1.639#.062
w/o LIMM
0.470=000 005
0.669 003
0.773=.003
0.349 000
3.021
1.120
w/o ATH
0.426 004
0.616 004
0.719m.004
0.207 000
3,284=-011
011
1.521
Event-T2Mh (Ours)
0.487 005
0.687 004
0,790 004
0.137 0033
2,928 010
1.010
w/o LIMM, ATII
0,366 008
0,547tt 009
0.664m.009 009
0.813 042
3.629
028
1853
w/o LIMM
0, 0.436=0007
0.652##006
0.753=:006
0.520
015
3,226
014
1.11844027
w/o ATHI
0.402 004
0.599 008
0.707 010
0.368 022
3,406=
016
1.606
Event-124 T2M (Ours)
0.466 008
0.660 008
0.767
0.265 007
3,063 015
1.039
Methods
MARDM-SIT 13.177
MARDM-DDPM
MoGinTT
MoMask 0.103
Light-T2M
ATT2M
Event- Wrent-T2Mce LLM execution time)
AIT (5)
3,038
0.973
0.142
3,853
0.167 (+1.430)
Table 17: Average inference time (AIT) comparison.
17 EXTENDED ABLATIONS AND ROLE OF EACH MODULE
To more thoroughly analyze the contribution of the newly introduced components beyond our main architecture, we conducted additional ablations on LIMM and ATII. Starting from the full Event- T2M model, we removed each module individually and retrained under identical settings. The results are summarized in [Table 16) In both cases, we observe consistent degradation in motion quality and text-motion alignment: FID becomes worse and R-Precision drops across all event- count subsets on HumanML3D-E, with the largest declines appearing on prompts with higher event complexity. This indicates that neither LIMM nor ATII is superficial add-on; both play mean- ingful role in stabilizing and aligning weentconditiones generation.
Conceptually, our goal is not to introduce entirely new low-level architectural primitives, but to de- sign an event-centric pipeline whose components are tailored to the structure induced by event-level conditioning. LIMM is used regularize local temporal dynamics and smooth transitions within and between event segments, preventing abrupt changes when strong event-level signals are injected. ATII adaptively injects global textual information in way that depends on the current motion con- text, helping the model decide when to rely more on event-local cues and when to fall back on global semantics. The ablation results in Table 16 show that removing either LIMM or ATII consistently harms both distributional quality and alignment, supporting our claim that these modules are integral parts of the event-aware design rather than generic, easily replaceable components.
A.18 COMPUTATIONAL COST AND LLM OVERHEAD
We evaluated the computational cost of Event-T2M on an NVIDIA A5000 GPU using an Average Inference Time (AIT) analysis that explicitly includes the latency introduced by the LLM-based event decomposition stage. To estimate the model-side inference cost, we randomly sampled 100 test captions and measured the time required to generate motions, obtaining an AIT of 0616 sec- onds per sample. We then separately measured the LLM lateney by applying our event decomposi- tion procedure to another set of 100 randomly selected test captions, which resulted in an average execution time of 1,4299 seconds per caption.
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Combining these two components yields total average cost of 1.5966 seconds per caption- motion pair. This analysis shows that, while Event-T2M, does introduce an additional LLM preprocessing step, the resulting overhead is moderate relative to the overall inference pipeline and can be clearly quantified. The detailed comparison with existing methods is summarized Table 17
28


Reviews:
[{"review_id": "DyNzWBJI8i", "reviewer": "Reviewer_hnW1", "summary": "This paper proposes an event-level text-to-motion generation benchmark and a stronger baseline for the task. To explicitly model the complexity of a target motion, the paper proposed to utilize an LLM to split the motion text prompts into event-level actions, where a larger number of events indicates harder cases. For a stronger baseline of the proposed task, an event-based cross-attention module is injected into the diffusion-based motion generation framework to improve the performance. Experimental results on the benchmark dataset validate the effectiveness of the proposed methods.", "questions": "See the weakness section.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The point of modeling the motion complexity by the number of events is straightforward and reasonable. The proposed HumanML3D-E benchmark will be beneficial to the community, which can evaluate motion generation frameworks on more detailed levels of complexity.\n- The experimental analysis of different methods on different event counts supports the motivation of the proposed event-based benchmark. \n- The design of the event-based cross-attention module is reasonable and validated by ablation studies.\n- The paper is well-written and easy to follow.", "weaknesses": "- The events of a motion are divided by an LLM with text input only. The label may contain errors. Manually validating the labels or sampling cases to check the accuracy rate of the LLM labels will be beneficial.\n- The paper misses some comparisons with some recent stronger baselines, e.g., MoGenTS (NeurIPS 2024), MARDM (CVPR 2025), and LAMP (ICLR 2025). \n- The event-based benchmark only contains one dataset, HumanML3D. It's better to add more datasets, e.g., KIT-ML, Motion-X, to better validate the generalizability of different methods.\n- Providing some failure cases of the proposed framework and previous work on complex scenarios, e.g., 4 events, will be beneficial.", "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": "v03iM4QyWB", "reviewer": "Reviewer_Kztd", "summary": "This paper addresses a key challenge in complex Text-to-Motion generation: the difficulty of existing models in handling prompts with multiple sub-motions, which often leads to omissions, merging, or reordering of motions. To solve this, the authors propose Event-T2M, a diffusion-based framework. The core idea is to first introduce a definition of an \"event\" as the smallest, semantically self-contained action unit in a text. They then use a Large Language Model (LLM) to decompose the input text into a sequence of these \"event\" clauses. These clauses are subsequently encoded by a TMR encoder (trained for motion-text alignment) into \"event tokens.\" Finally, these event tokens are injected into a Conformer-based diffusion model via a novel \"Event-based Cross-attention\" (ECA) module to guide the generation of the motion sequence. Furthermore, to specifically evaluate the model's ability to handle complex prompts, the authors construct a new benchmark, HumanML3D-E, which stratifies the HumanML3D test set by the number of events in the text. Experimental results show that Event-T2M achieves comparable performance to SOTA on standard benchmarks (HumanML3D, KIT-ML) but significantly outperforms baselines on the new HumanML3D-E benchmark, especially as event complexity increases.", "questions": "- Can you evaluate your model on a test set that was not constructed using your LLM pipeline? On a complex motion test set where events were manually segmented and temporally aligned by human annotators, would Event-T2M still show an advantage over baselines?\n- For a fair comparison, if you were to replace the CLIP encoder in the baseline models (like AttT2M or MoMask) with the TMR encoder (and use TMR's word-level tokens as K/V), how much would their performance on HumanML3D-E improve? As I know, replacing the CLIP encoder with TMR encoder will significantly improve the performance.\n- What are the differences between Event-T2M and other motion generators using LLM for decomposition?\n\n**I will raise the score to a positive mark if you can address my concerns regarding the \"unfair comparison\"** (mainly concerning the experimental setup for the TMR encoder and the aspects mentioned in the Weaknesses section).", "limitations": "", "rating": 2, "confidence": 5, "soundness": 1, "presentation": 3, "contribution": 1, "strengths": "- The problem significance is huge. Generating complex and consistent human motions is an unsolved challenge in the T2M field.\n- This paper proposes a novel benchmark called HumanML3D-E. This is the first benchmark stratified by the \"event complexity\" of the prompts. It provides a very valuable evaluation tool for future research on long and complex T2M generation field.\n- The idea of decompose the complex motions is very intuitive and logical.", "weaknesses": "- **Unfair Comparison**: The authors' new benchmark, HumanML3D-E, is constructed using an LLM and a specific \"event-aware prompt.\" However, the proposed model, Event-T2M, **also relies on the exact same LLM and the exact same prompt** in its data preprocessing stage. Event-T2M is evaluated on a test set that is perfectly aligned with its own training and inference pipeline. In contrast, all baseline models are evaluated without using this LLM-based event decomposition preprocessing. This constitutes an extremely unfair comparison. The poor performance of the baselines on HumanML3D-E is likely just an artifact of their input representation (e.g., CLIP-based word tokens) being mismatched with the benchmark's construction (LLM-based clauses), not because their architectures inherently fail at complexity.\n\n- **Limited Technical Novelty**: I must point out that there are already some methods trying to solve the generation of the long motions using LLM. For instance, the recent ATOM[1] framework uses GPT-4 to construct event-level prompts and GPT-4V as an AI reward model to fine-tune a generator, specifically targeting event-level alignment (integrity, temporal order, and frequency). Additionally, InstructMotion[2] explicitly uses an LLM to generate long prompts, subsequently using Reinforcement Learning (RL) to fine-tune an autoregressive motion generator. **Worryingly, these highly relevant prior works, which also tackle event-level or complex alignment using LLMs, are not cited or discussed in the paper's Related Works.**\n\n[1] Han H, Wu X, Liao H, et al. Atom: Aligning text-to-motion model at event-level with gpt-4vision reward[C]//Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 22746-22755.\n[2] Mao, Yunyao, et al. \"Learning generalizable human motion generator with reinforcement learning.\" arXiv preprint arXiv:2405.15541 (2024).", "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": "sKYwdnQ0oN", "reviewer": "Reviewer_5wEr", "summary": "This work introduces Event-T2M, a diffusion-based framework that decomposes complex text prompts into semantically self-contained events and generates motion through event-based cross-attention. It also builds HumanML3D-E, the first benchmark stratified by event count, and demonstrates that Event-T2M maintains state-of-the-art performance while significantly improving motion coherence and naturalness for multi-event prompts.", "questions": "See weaknesses.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1.Proposes an event-based paradigm for motion generation.\n\n2.Constructs the first event-level motion generation dataset.", "weaknesses": "1.Does event-driven motion generation offer advantages over action-driven or hybrid (action + event) methods?\n2.Does the proposed method outperform approaches that enhance motion quality through motion retrieval?\n3.In TMR, innovation based solely on input differences does not constitute true novelty.\n4.LIMM, ATII, and ECA follow common module design patterns and lack sufficient originality.", "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": "mG1KqYyoHb", "reviewer": "Reviewer_mvTh", "summary": "The paper proposes Event-T2M—a diffusion framework that decomposes prompts into multi-events, encodes them with a TMR encoder, and integrates them via event-based cross-attention in Conformer blocks. The authors also build HumanML3D-E, a benchmark stratified by event count. Experiments show Event-T2M matches SOTA on standard tests (HumanML3D, KIT-ML) and outperforms baselines as event complexity rises.", "questions": "1. In Equations (3) and (7), a coefficient of 0.5 is applied to the residual term. It remains unclear why this specific coefficient was chosen instead of 1, and the rationale behind this design choice warrants further explanation.\n2. The efficiency analysis appears to overlook the computational overhead introduced by the Large Language Model (LLM). While the baseline model (e.g., Momask) does not employ an LLM for text segmentation, the proposed Event-T2M model utilizes an LLM to partition the input text into events. The time cost associated with this LLM processing stage should be accounted for in the overall efficiency evaluation.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper solves multi-action prompt mishandling via a principled \"event\" definition and Event-T2M (with event decomposition, TMR encoding, and ECA module), avoiding action issues like omissions. Authors also build HumanML3D-E, the first event-count-stratified benchmark, fixing existing benchmark gaps. This paper also provides solid experiments (matching SOTA on HumanML3D/KIT-ML, outperforming baselines on complex HumanML3D-E) and user studies validating event definition, benchmark reliability, and motion quality.", "weaknesses": "The ablation analysis in this paper is limited in scope. Although multiple new modules are proposed (e.g., LIMM, ATII, Conformer, ECA), the experiments focus solely on the ECA module and the text encoder, failing to assess the necessity and individual impact of the other introduced components.", "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)