UniMate

One Unified Model to Animate Diverse Skeletons (SIGGRAPH Asia 2026)

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Pretrained checkpoints for UniMate, a text-conditioned flow-matching model that generates motion for skeletons of arbitrary topology: animals, humanoids and rigged objects.

Models

¹ Denoiser only. The frozen google/flan-t5-base text encoder is downloaded from the Hub on first use.

Use each model's final checkpoint, checkpoints/checkpoint_step_<steps>.pt.

v2 versus preview. Raising the joint limit from 60 to 70 keeps 98.1% of the training clips instead of 86.9%, and 70 of the 74 Truebones species. Datasets are balanced before object types (sampler_dataset_alpha = 0.25), so Truebones, Mixamo and Objaverse-XL make up about 13%, 24% and 63% of the training samples; the single Mixamo rig previously got under 1%.

Mixamo humanoids. To test humanoid animation on the Mixamo rig, use unimate_mixamo_f60. It is trained on the 2,162 Mixamo clips alone, all on the one 22-joint rig, without topology augmentations. It animates that rig only; use a UniML3D model for any other skeleton.

Model details

Quick start

Run every command from the root of the code repository, with its unimate environment installed.

1. Build the dataset features. The sampler reads each target skeleton from dataset/features/<dataset>/. The Hub dataset ships the stage 1-3 export but not these stage-4 features, so build them from the revision the model was trained on:

hf download Linzhan/UniML3D --repo-type dataset \
    --revision faaa81773b315247b03f183548e3898dbec2ce80 --local-dir dataset
bash data_process/scripts/run_extract_features.sh truebones   # likewise mixamo, objaverse

unimate_mixamo_f60 needs only the mixamo features.

The Truebones motions come from a commercial pack and are not on the Hub; the dataset card explains how to rebuild them.

2. Download a model. This fetches the configuration, normalization statistics and final checkpoint of the recommended model:

hf download Linzhan/UniMate \
    --include "unimate_uniml3d_f60_v2/*.json" "unimate_uniml3d_f60_v2/*.npy" \
              "unimate_uniml3d_f60_v2/checkpoints/checkpoint_step_100000.pt" \
    --local-dir outputs

To test Mixamo humanoid animation, fetch unimate_mixamo_f60 instead:

hf download Linzhan/UniMate \
    --include "unimate_mixamo_f60/*.json" "unimate_mixamo_f60/*.npy" \
              "unimate_mixamo_f60/checkpoints/checkpoint_step_120000.pt" \
    --local-dir outputs

3. Generate motion from text. Write the prompts to test_cases.json with keys <object_type>-<case_id>. object_type is a skeleton in dataset/features/: a Truebones species, mixamo, or an Objaverse-XL object ID. case_id is a free-form tag that names the output files. With unimate_mixamo_f60, every key is mixamo-<case_id> and --exp_dir is outputs/unimate_mixamo_f60.

{
  "Horse-0": "An object rears up on its hind legs.",
  "mixamo-0": "An object jumps in place with both arms raised.",
  "144367de23534c28ad2e83fc8abbd9ea-0": "An object flaps its wings."
}

python -m unimate.inference.sample \
    --exp_dir outputs/unimate_uniml3d_f60_v2 \
    --test_cases_json test_cases.json \
    --num_repetitions 3

The sampler loads the EMA weights of the latest checkpoint in checkpoints/. It writes the motions (.npy, shape (60, J, 12) for a skeleton with J joints) and skeleton renders (.mp4) to outputs/unimate_uniml3d_f60_v2/samples/. In-betweening, joint-level editing, motion expansion and driving a rigged mesh are documented in the code README.

Training

² Resumed once from the step-60k checkpoint.

Resume from a released checkpoint, or train from scratch:

bash scripts/run_train.sh configs/uniml3d_60frames_graph_adaln_v2.json -- \
    --resume outputs/unimate_uniml3d_f60_v2/checkpoints/checkpoint_step_100000.pt

accelerate launch --num_processes 8 -m unimate.training.train \
    --config configs/uniml3d_60frames_graph_adaln_v2.json

Repository layout

config.json                        index of the released models
LICENSE
<model>/
  config.json                      resolved training configuration; read by inference
  dataset_stats.npy                feature normalization statistics
  checkpoints/
    checkpoint_step_<N>.pt         model and EMA weights, optimizer and scheduler state; every 10k steps
  logs/                            TensorBoard curves, one event file per training job
  samples/
    step_<NNNNNN>/                 motions generated during training (step_000000: before training)
      <object_type>-<i>_fk.mp4     joints from forward kinematics of the predicted rotations
      <object_type>-<i>_ric.mp4    predicted joint positions

Limitations

• Clip length: a sample is a fixed 60-frame window (2 seconds). Longer motion requires chaining samples with the expansion application.
• Joint count: the v2 models are trained on skeletons with at most 70 joints, which excludes four Truebones species (Bear, Centipede, Monkey, Dragon); the preview model's limit is 60. Skeletons with more than 71 joints (61 for the preview model) cannot be sampled. unimate_mixamo_f60 has seen only the 22-joint Mixamo rig and cannot sample a larger skeleton.
• New skeletons: there is no skeleton-only input. A new rig must be converted into a feature directory with data_process/scripts/run_preprocess_char.sh, and needs at least one animation clip.
• Text: every training caption has the subject "An object"; the skeleton determines the character. Prompts should describe the motion only.

License

The checkpoints and the UniMate code are released under the MIT License. The training data remain under their source licenses: Adobe's Mixamo terms of use, the per-object licenses of Objaverse-XL, and the commercial Truebones ZOO license. Review these terms before using the models.

Citation

@article{mou2026unimate,
  title   = {UniMate: One Unified Model to Animate Diverse Skeletons},
  author  = {Mou, Linzhan and Lei, Jiahui and Dou, Zhiyang and Cai, Chenyue and Song, Chaoyue and Finkelstein, Adam and Rusinkiewicz, Szymon},
  journal = {arXiv preprint arXiv:2609.05415},
  year    = {2026}
}