VerMo is a Motius-native research baseline rather than a reproduction of an
external paper. The released M2T checkpoint uses a Llama-3.2-1B-Instruct
language backbone, a 16K motion tokenizer, and an explicit SMPL-22 motion
processor. No external paper or original repository is claimed for this row.
1import numpy as np
2from motius.pipelines.vermo import VermoPipeline
3
4pipe = VermoPipeline.from_pretrained(
5 "ZeyuLing/Motius-VerMo-HumanML3D",
6 bundle_kwargs={"device": "cuda"},
7 smpl_model_dir="checkpoints/body_models/smpl",
8)
9motion = np.load("sample.npy") # denormalized HumanML3D-263
10caption = pipe.infer_m2t([motion], lengths=[len(motion)])[0]
VerMo-138 stores absolute root translation (3), frame-to-frame root
translation (3), and 22 local joint rotations in column-major 6D form (132).
HumanML3D inputs are recovered to SMPL-22 joints, solved to motion135 with
position IK, then repacked explicitly from row-major to column-major 6D.
1from motius import Pipeline
2
3pipeline = Pipeline.from_pretrained("ZeyuLing/Motius-VerMo-HumanML3D")