Most existing human motion generation (HMG) methods use cross-attention modules to inject text semantics, but ignore the importance of bidirectional modeling between motion and text tokens, which limits text comprehension. A straightforward idea is introducing multimodal diffusion transformers (MMDiT), which have shown effective joint text--visual modeling in vision generation, into HMG. However, we find that articulated motion is temporally coherent but weakly correlated across joints, in which directly applying an MMDiT with flow matching produces poorly coordinated and jerky motion. In this work, we propose Timo, a novel kinematics-aware MMDiT framework tailored for HMG. Timo combines fully shared multimodal attention for bidirectional text--motion modeling with flow matching, geometric and rotational-kinematics supervision that compares actual rotations and their changes over time, and a two-stage curriculum progressing from broad motion learning to detailed caption alignment. Further, we construct a benchmark of 40,025 held-out clips from six public datasets spanning diverse actions, assessing six complementary dimensions under a common evaluator and scoring protocol. Our model substantially outperforms state-of-the-art methods in both quantitative and qualitative evaluations. Remarkably, Timo surpasses Kimodo on five of six dimensions, achieving a 40.8% relative improvement in the average benchmark score. Project page: https://kyfafyd.wang/projects/timo. Demo page: https://timo.kyfafyd.wang.
Figures & tables
Figure 1: Comparison on our six-dataset benchmark. Timo and four released systems: MotionMillion ( Fan et al., 2025 ) , HY-Motion ( Wen et al., 2025 ) , GENMO ( Li et al., 2025 ) and Kimodo ( Rempe et al., 2026 ) .
Figure 2: Kinematics-aware multimodal generation. (a) Text and motion tokens enter one 24 -block stack as a single sequence predicting clean body parameters x^0 directly. (b) A block shares QKV, MLP and adaLN ( Peebles and Xie, 2023 ) weights across modalities. (c) Reconstruction, rotational kinematics and decoded geometry provide complementary supervision. At right, predictions with equal reconstruction loss but different temporal errors, distinguished by Lkin .
Figure 3: Corpus composition. (a) Motion categories over all 1,983 hours; the three rings are family, subfamily and action, and arc is hours. (b) Caption length over all corpora, weighted by hours. (c) Our own captions’ vocabulary. Hours include mirrored augmentation and held-out splits.
Method
Venue
Params
Nat. ↑
Match ↑
Div. ↑
Smth. ↑
Speed ↑
Plaus. ↑
Avg. ↑
MotionMillion ( Fan et al., 2025 )
ICCV’25
7.8 B
42.3
30.5
51.7
49.5
45.5
72.1
48.6
HY-Motion ( Wen et al., 2025 )
arXiv’25
1.0 B
38.0
52.6
67.3
54.3
31.4
71.4
52.5
GENMO ( Li et al., 2025 )
ICCV’25
523 M
44.2
28.0
52.3
79.8
20.6
68.1
48.8
Kimodo ( Rempe et al., 2026 )
arXiv’26
283 M
36.8
51.0
59.1
79.0
49.5
93.4
61.5
Timo (ours)
495 M
86.4
86.8
89.3
90.7
81.4
85.0
86.6
Table 1: Comparison on our six-dataset benchmark. Released multi-corpus systems are re-evaluated, not quoted from their papers. Training data and representations differ; ours includes in-house capture. Axes are custom 0 – 100 scores, higher better; differences and relative gains refer to these scores, not the underlying raw metrics. Params excludes frozen text encoders. Avg. is the unweighted mean of the six scores.
Figure 4: Qualitative comparison. One model per row, one prompt per column, moments left to right, shaded light to dark as time advances; a column shares one camera, so a body that sits lower or reaches further has really done so. Fig. S2 carries two further prompts.
Table 6Table 7
Figure 5: Long-sequence application: twelve prompts over 36 seconds. Travelling and in-place prompts alternate, and Timo generates them as a single motion, drawn in one scene with a common camera and scale. Displayed poses are aligned to the floor and shaded light to dark with time. Arrows span the travelling prompts.
Figure 6: Execution on two physical humanoids. Left: LimX Luna performs a guiding gesture. Right: LimX Oli walks, squats and stands. Generated references are retargeted and tracked by the robot controller; Fig. S4 provides the full examples.
Appendix figures & tables8 assets
Supplementary material from the paper’s appendix.
Appendix
Setting
Value
Backbone
24 blocks, width 1024 , 16 heads, 495 M parameters
100 k steps per stage; rate halved at 50 k and 80 k
Appendix
Table S1: Model and optimisation settings shared by both training stages.
Group
Term
Weight
Reconstruction
Clean-motion error; min-SNR truncation γ=5
1
Root rotation
Absolute / velocity / acceleration
5/10/10
Body rotation
Velocity / acceleration
1/1
Geometry
3D joints / vertices
500/500
2D reprojection
1000
Translation / contact BCE
1/1
Appendix
Table S2: Objective weights. The objective is unchanged between stages. Huber terms use β=0.05 ; contact labels use a foot-velocity threshold of 0.15 m/s.
Dataset
Source
Caption
Tier
Hours †
HumanML3D ( Guo et al., 2022 )
studio
clip
fine
52.8
BABEL ( Punnakkal et al., 2021 )
studio
clip
fine
77.7
AIST++ ( Li et al., 2021 )
studio
clip
fine
0.4
GRAB ( Taheri et al., 2020 )
studio
clip
fine
7.5
PerMo ( Kim et al., 2025 )
studio
clip
fine
17.0
BONES ( Luo et al., 2026 )
studio
clip
fine
152.3
Appendix
Table S3: Corpora. Source is the acquisition route, studio capture or in-the-wild video reconstruction, and Caption what the text describes: a clip, a vision–language model’s label, or a multi-minute take. StudioTake and StudioClip are our own in-house capture ( Fig. 3 ); every other row is a public corpus. † Hours include left–right mirroring across train and held-out splits; BONES already contains mirror twins and is counted once.
Method
R@1 ↑
R@2 ↑
R@3 ↑
Seg. FID ↓
Trans. FID ↓
MM-Dist ↓
Pen. ↓
MotionMillion
25.3
36.9
44.3
0.201
0.409
1.237
13.31
HY-Motion
38.9
54.0
62.7
0.233
0.478
1.187
13.90
GENMO
22.7
34.6
43.0
0.212
0.338
1.236
16.83
Kimodo
37.4
53.1
62.6
0.319
0.364
1.199
1.42
Timo
62.1
77.6
85.2
0.053
0.034
1.055
4.93
Appendix
Table S4: Raw metrics underlying the main comparison. R@1/2/3 are percentages within 32 -candidate retrieval groups. Fréchet distances (FID) and text–motion distance (MM-Dist) use the same six-corpus evaluator for every row, so they are not directly comparable to numbers from a differently trained evaluator. Pen. is the capsule-interpenetration proxy, multiplied by 103 for readability, not a collision frequency.
Figure S1: Additional long-sequence examples. Top: five prompts over approximately 20 seconds. Bottom: twelve prompts over 36 seconds. As in Fig. 5 , poses share a scene and darken with time; each displayed pose is aligned to the floor. The lower sequence also includes motion postprocessing ( Section D.1 ).
Figure S2: Additional qualitative comparisons. Waving and stepping prompts across five methods, with six displayed poses per result and a common body scale.
Figure S3: Timo across fourteen prompts : six moments each, shaded light to dark. One panel height is fixed for the whole figure, so the 0.5 m grid is the same size in every cell and amplitude compares across prompts as well as within one.
Figure S4: Four motions executed on physical humanoids. Samples from Timo, retargeted to the robot’s rig and tracked by the whole-body controller of Fu et al. (2026) ; eight moments per row. The first two rows are a LimX Luna ( LimX Dynamics, 2026 ) and the last two a LimX Oli ( LimX Dynamics, 2025 ) . The generator is shared across platforms; retargeting and tracking adapt its outputs to each robot. Robot images are not shown at a common physical scale.