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ZeyuLing/
hftrainer-motiongpt3-humanml3d

Name: hftrainer-motiongpt3-humanml3d
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MotionGPT3 - Human Motion as a Second Modality

Text-to-motion baseline integrated into the hftrainer Model Zoo. The runtime is self-contained under hftrainer.models.motion.motiongpt3.network and does not import the original repository at inference time.


Task	Text-to-Motion (T2M), motion-language generation
Bundle / Pipeline	`MotionGPT3Bundle` / `MotionGPT3Pipeline`
Processed HF artifact	`ZeyuLing/hftrainer-motiongpt3-humanml3d`
Motion representation	HumanML3D-263 (263-dim, 20 fps, 22 joints)
Architecture	Motion-language model with MotionGPT3 VAE and MoT-GPT2 adapter
Paper	MotionGPT3: Human Motion as a Second Modality, OpenMotionLab - arXiv:2506.24086
Original code	https://github.com/OpenMotionLab/MotionGPT3

Weights

Self-contained hftrainer artifact:

Artifact	Location	Contents	Status
MotionGPT3 HumanML3D	`ZeyuLing/hftrainer-motiongpt3-humanml3d`	`motiongpt3.ckpt` + `configs/` + `assets/meta/{mean,std}.npy` + `deps/mot-gpt2/` + `model_index.json`	public Hub artifact
local mirror	`checkpoints/baselines/motiongpt3`	same layout	optional local cache

Use directly from the Hub:

python

from hftrainer.pipelines.motiongpt3 import MotionGPT3Pipeline

pipe = MotionGPT3Pipeline.from_pretrained(
    "ZeyuLing/hftrainer-motiongpt3-humanml3d",
    device="cuda",
)
motions = pipe.infer_t2m(
    ["a person walks forward then sits down"],
    [120],
)  # list of (T, 263)

For a local mirror:

pipe = MotionGPT3Pipeline.from_pretrained("checkpoints/baselines/motiongpt3", device="cuda")

Motion Representation

MotionGPT3 natively generates HumanML3D-263 at 20 fps. For shared SMPL and MotionStreamer-272 evaluation, use the validated bridge:

HumanML3D-263 -> SMPL motion_135 via IK refine-80 -> MotionStreamer-272

The artifact includes the local mot-gpt2 adapter and HumanML3D statistics so the published pipeline can be restored without an external runtime checkout.

HumanML3D Leaderboard Metrics

The row below uses the shared HumanML3D official-test caption protocol and the HML263 round-trip GT reference for SMPL-based evaluators.

Evaluator	R1 up	R2 up	R3 up	FID down	MM down	Div up
MotionStreamer-272	0.6709	0.8242	0.8817	20.9913	17.5664	25.6889
MotionCLIP-135 no-L2	0.4894	0.6570	0.7455	91.0385	41.5060	23.0747

Physical metrics:

Slide down	Float down	Jitter down	Dynamic down
3.8137	9.6933	4.7599	23.1948

Implementation Notes

Artifact inference imports only hftrainer.models.motion.motiongpt3.network.
The bundle patches the small transformer compatibility fields required by newer transformers versions before loading the released checkpoint.
The validated HumanML3D setting uses the test generation stage and temperature 1.0.