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Motius-MotionGPT3-HumanML3D
ZeyuLing/Motius-MotionGPT3-HumanML3D
Motius-MotionGPT3-HumanML3D is a other model from ZeyuLing. Use it for the other task on the model card, and read the license before you ship it in a product. It is set up for motius.
<h1 align="center"MotionGPT3 Model Card</h1
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Public
Updated Jul 25, 2026
Repo size
1.7 GB
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Repo makeup
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.ckpt1.2 GB · 71%
At a glance
- Task
- other
- Library
- motius
- Access
- Public
- Created
- Jul 19, 2026
- Updated
- Jul 25, 2026
- SHA
- 872697d5
Model card
From the Hugging Face model README
<h1 align="center">MotionGPT3 Model Card</h1>
<p align="center">
<strong>A continuous-latent bimodal motion-language model, packaged for HumanML3D motion captioning.</strong>
</p>
<p align="center">
<a href="https://arxiv.org/abs/2506.24086">Paper</a> |
<a href="https://motiongpt3.github.io/">Project Page</a> |
<a href="https://github.com/OpenMotionLab/MotionGPT3">Original GitHub</a> |
<a href="https://huggingface.co/ZeyuLing/Motius-MotionGPT3-HumanML3D">Motius Checkpoint</a>
</p>
MotionGPT3 separates text and motion processing into modality-specific branches
with shared attention. Unlike tokenized motion-language models, it represents
motion in a continuous VAE latent space. The Motius artifact packages the final
official multi-task checkpoint and all model/tokenizer configuration required by
Pipeline.from_pretrained.
Release Snapshot
| Item | Value |
|---|---|
| Released task | M2T |
| Input representation | HumanML3D-263, 20 fps |
| Motion latent | Continuous temporal VAE latent |
| Language model | GPT-2-family bimodal Transformer |
| Checkpoint provenance | Official final MotionGPT3 checkpoint |
| Checkpoint | ZeyuLing/Motius-MotionGPT3-HumanML3D |
| Pipeline | motius.pipelines.motiongpt3.MotionGPT3Pipeline |
Usage
import numpy as np
from motius.pipelines.motiongpt3 import MotionGPT3Pipeline
pipe = MotionGPT3Pipeline.from_pretrained(
"ZeyuLing/Motius-MotionGPT3-HumanML3D",
bundle_kwargs={"device": "cuda"},
)
motion = np.load("sample.npy") # denormalized HumanML3D-263
caption = pipe.infer_m2t([motion], lengths=[len(motion)])[0]
M2T Evaluation
| Protocol | Samples | BLEU-4 | ROUGE-L | CIDEr | BERT F1 | R@1 | R@2 | R@3 | Matching |
|---|---|---|---|---|---|---|---|---|---|
| HumanML3D M2T | 4,400 | - | - | - | - | - | - | - | - |
Motius Components
| Component | Path |
|---|---|
| Pipeline | motius/pipelines/motiongpt3/pipeline.py |
| Bundle | motius/models/motiongpt3/bundle.py |
| Bimodal GPT runtime | motius/models/motiongpt3/mot_example_gpt2_sepattn.py |
| Generation runtime | motius/models/motiongpt3/mot_example_gpt2_sepattn_gen.py |
Citation
@misc{zhu2025motiongpt3,
title={MotionGPT3: Human Motion as a Second Modality},
author={Zhu, Bingfan and Jiang, Biao and Wang, Sunyi and Tang, Shixiang and Chen, Tao and Luo, Linjie and Zheng, Youyi and Chen, Xin},
year={2025},
eprint={2506.24086},
archivePrefix={arXiv}
}
Direct Loading
from motius import Pipeline
pipeline = Pipeline.from_pretrained("ZeyuLing/Motius-MotionGPT3-HumanML3D")
- Task
- other
- Library
- motius
- Created
- Jul 19, 2026
- Updated
- Jul 25, 2026