--- title: "UniMate" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/d550a449-8fde-4be7-9fe9-9c24171348d3" updated: "2026-11-12" --- # UniMate > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates motion for skeletons of any shape, from animals to humanoids to rigged objects, from a text description. Suits animation researchers and technical artists, under a non-commercial license. UniMate is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Linzhan ## About ### What it is UniMate turns a text prompt into motion for a skeleton of any shape, whether an animal, a humanoid or a rigged object. It is built on flow matching. This repo holds its pretrained checkpoints. The paper behind it is titled One Unified Model to Animate Diverse Skeletons. ### Who it suits Animation researchers and technical artists who want to turn a written motion description into movement for a rigged character or creature. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Animation | Technical artists | Generate motion for a rigged character from text | Make a quadruped trot forward | | Games | Prototype teams | Test movement ideas on varied skeletons | Animate an eagle or a shark asset | | Research | Motion researchers | Study one model across many skeleton types | Compare checkpoints on the same prompt | If you need to use it commercially, you can skip it, since the license is non-commercial. ### Where to use it - The UniMate code repository, in its own Python environment - Rigged GLB, glTF or FBX assets after the card's preprocessing step - Windows, macOS or Linux, for downloading the files | Use case | Who | What they get back | | --- | --- | --- | | Text to motion | Animators | 60 frames at 30 fps of joint motion | | Animate a mesh | Technical artists | One GLB or FBX per motion | | Mixamo or Truebones rigs | Researchers | Dedicated checkpoints for those skeleton sets | ### How to set it up - Python with pip - The separate UniMate code repository and its environment - The checkpoint you want, recommended is unimate\_uniml3d\_f60\_v3 1. Install the Hugging Face hub package. 2. Download the repo, or only the model folder you need. 3. From the code repository's root, run the generation script on an asset. ```sh pip install -U huggingface_hub hf download Linzhan/UniMate ``` ### How to use it From the code repository's root, download one model, generate motion for an example asset with a text prompt, then drive the asset's mesh with the motions. ```bash hf download Linzhan/UniMate --repo-type model --local-dir outputs \ --include "unimate_uniml3d_f60_v3/*.json" --include "unimate_uniml3d_f60_v3/*.npy" \ --include "unimate_uniml3d_f60_v3/checkpoints/checkpoint_step_150000.pt" ``` ```bash python -m unimate.inference.sample --exp_dir outputs/unimate_uniml3d_f60_v3 --asset assets/examples/unitree_go2 \ --prompt "An object trots forward." "An object rears up on its hind legs." --num_repetitions 3 \ --output_dir outputs/samples/custom ``` ```bash bash scripts/run_animate_motion.sh outputs/samples/custom ``` | Tool | What it does | | --- | --- | | unimate.inference.sample | Generates motion from text for an asset | | scripts/run\_animate\_motion.sh | Drives each asset's mesh with its motions | | data\_process.rig\_preprocess | Labels the joints of your own rigged file | ### Example You run the sample command on the example quadruped asset with the prompts that it trots forward and that it rears up. You get three repetitions of each, and the last script turns each motion into a mesh file. ### For newcomers and experts If you are new, run the three commands on the shipped example asset first. Then try your own rig through the preprocessing step, which stops so you can check the joint labels. The recommended model is a transformer denoiser with graph attention and text conditioning through AdaLN, trained with flow matching on skeletons of 5 to 70 joints. Text comes from a frozen flan-t5-base encoder, downloaded on first use. Output is position, rotation relative to the T-pose and velocity for each joint. A text prompt and a skeleton go in → flan-t5-base encodes the text → The denoiser runs flow matching → 60 frames of joint motion come out → A script drives the mesh ### Good to know - The Mixamo checkpoint works for one rig only, the 22-joint Mixamo skeleton - The Truebones model covers 73 of 74 species, all but Dragon - License: CC BY-NC 4.0, so non-commercial use only - Cost to run: none named, you supply the hardware - Data it reaches: your prompts and rigged assets, and the files downloaded from Hugging Face - Not for: commercial use, under the non-commercial license Denoiser parameters in millions | Label | Value | | --- | --- | | unimate\_uniml3d\_f60\_v3 | 74.1 million | | unimate\_uniml3d\_f60\_v3\_cross\_attn | 66.2 million | | unimate\_mixamo\_f60\_v2 | 47.8 million | | unimate\_truebones\_f60\_v2 | 47.8 million | ## README ````markdown # UniMate Generates motion for skeletons of any shape, from animals to humanoids to rigged objects, from a text description. Suits animation researchers and technical artists, under a non-commercial license. Made by Linzhan on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: UniMate turns a text prompt into motion for a skeleton of any shape, whether an animal, a humanoid or a rigged object. It is built on flow matching. This repo holds its pretrained checkpoints. The paper behind it is titled One Unified Model to Animate Diverse Skeletons. Who it suits: Animation researchers and technical artists who want to turn a written motion description into movement for a rigged character or creature. ```table Industry | Who uses it | How they use it | Example Animation | Technical artists | Generate motion for a rigged character from text | Make a quadruped trot forward Games | Prototype teams | Test movement ideas on varied skeletons | Animate an eagle or a shark asset Research | Motion researchers | Study one model across many skeleton types | Compare checkpoints on the same prompt ``` If you need to use it commercially, you can skip it, since the license is non-commercial. Where to use it: - The UniMate code repository, in its own Python environment - Rigged GLB, glTF or FBX assets after the card's preprocessing step - Windows, macOS or Linux, for downloading the files ```table Use case | Who | What they get back Text to motion | Animators | 60 frames at 30 fps of joint motion Animate a mesh | Technical artists | One GLB or FBX per motion Mixamo or Truebones rigs | Researchers | Dedicated checkpoints for those skeleton sets ``` How to set it up: - Python with pip - The separate UniMate code repository and its environment - The checkpoint you want, recommended is unimate_uniml3d_f60_v3 1. Install the Hugging Face hub package. 2. Download the repo, or only the model folder you need. 3. From the code repository's root, run the generation script on an asset. ```sh pip install -U huggingface_hub hf download Linzhan/UniMate ``` How to use it: From the code repository's root, download one model, generate motion for an example asset with a text prompt, then drive the asset's mesh with the motions. ```bash hf download Linzhan/UniMate --repo-type model --local-dir outputs \ --include "unimate_uniml3d_f60_v3/*.json" --include "unimate_uniml3d_f60_v3/*.npy" \ --include "unimate_uniml3d_f60_v3/checkpoints/checkpoint_step_150000.pt" ``` ```bash python -m unimate.inference.sample --exp_dir outputs/unimate_uniml3d_f60_v3 --asset assets/examples/unitree_go2 \ --prompt "An object trots forward." "An object rears up on its hind legs." --num_repetitions 3 \ --output_dir outputs/samples/custom ``` ```bash bash scripts/run_animate_motion.sh outputs/samples/custom ``` ```table Tool | What it does unimate.inference.sample | Generates motion from text for an asset scripts/run_animate_motion.sh | Drives each asset's mesh with its motions data_process.rig_preprocess | Labels the joints of your own rigged file ``` Example: You run the sample command on the example quadruped asset with the prompts that it trots forward and that it rears up. You get three repetitions of each, and the last script turns each motion into a mesh file. For newcomers and experts: If you are new, run the three commands on the shipped example asset first. Then try your own rig through the preprocessing step, which stops so you can check the joint labels. The recommended model is a transformer denoiser with graph attention and text conditioning through AdaLN, trained with flow matching on skeletons of 5 to 70 joints. Text comes from a frozen flan-t5-base encoder, downloaded on first use. Output is position, rotation relative to the T-pose and velocity for each joint. ```flow A text prompt and a skeleton go in flan-t5-base encodes the text The denoiser runs flow matching 60 frames of joint motion come out A script drives the mesh ``` Good to know: - The Mixamo checkpoint works for one rig only, the 22-joint Mixamo skeleton - The Truebones model covers 73 of 74 species, all but Dragon - License: CC BY-NC 4.0, so non-commercial use only - Cost to run: none named, you supply the hardware - Data it reaches: your prompts and rigged assets, and the files downloaded from Hugging Face - Not for: commercial use, under the non-commercial license ```chart Title: Denoiser parameters in millions unimate_uniml3d_f60_v3: 74.1 million unimate_uniml3d_f60_v3_cross_attn: 66.2 million unimate_mixamo_f60_v2: 47.8 million unimate_truebones_f60_v2: 47.8 million ``` ## On Hugging Face, 13 November 2026 - Library: pytorch - Downloads in the last 30 days: 533 - Likes: 97 - License: cc-by-nc-4.0 - Languages: en - Quantized versions: 1 - Fine-tunes: 3 - Tags: pytorch, motion-generation, text-to-motion, character-animation, skeletal-animation, flow-matching, unimate ## Links - Model: https://huggingface.co/Linzhan/UniMate - Files: https://huggingface.co/Linzhan/UniMate/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Linzhan/UniMate ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [Linzhan/UniMate on Hugging Face](https://huggingface.co/Linzhan/UniMate) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | UniMate (this page) | 533 | 97 | — | cc-by-nc-4.0 | 2026-10-07 | | [Cola DLM](https://mindket.com/p/b4dd84c1-dd8f-48de-b432-c7071e24c476) | 93 | 51 | — | apache-2.0 | 2026-06-18 | | [RFD4-Proteina](https://mindket.com/p/e89dbf1d-b52e-4519-a7fe-5a631e5f48ef) | 14,235 | 45 | 436M | bsd-3-clause | 2026-10-30 | | [RFD4-Proteina-Diffusers](https://mindket.com/p/9dbcd74f-6cd7-4625-8a43-b13ac25d7e05) | 403 | 5 | 178M | bsd-3-clause | 2026-10-20 | | [NLLB-200 3.3B](https://mindket.com/p/a42b68a2-49a1-4575-8eb2-c6dbd19eaacf) | 293,185 | 493 | — | cc-by-nc-4.0 | 2023-02-11 | | [Hy-Nomas](https://mindket.com/p/9a697cce-2966-4f2d-bb74-d8d6874fbecb) | 90,896 | 153 | — | apache-2.0 | 2026-11-04 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · AI без границ · 2026-10-02](https://bsky.app/profile/aibiznes.bsky.social/post/3mwuqyphnwo2g): UniMate — одна модель для анимации любых риггованных персонажей по тексту Нейросеть генерирует анимацию для разных скелетов:... https://github.com/Friedrich-M/UniMate https://linzhanmou.com/unimate/ https://huggingface.co/Linzhan/UniMate https://linzhanmou.com/unimate/interactive.html - [Bluesky · arXiv cs.CV Computer Vision and Pattern Recognition · 2026-09-07](https://bsky.app/profile/cscv-bot.bsky.social/post/3muvvnohah324): Linzhan Mou, Jiahui Lei, Zhiyang Dou, Chenyue Cai, Chaoyue Song, Adam Finkelstein, Szymon Rusinkiewicz: UniMate: One Unified Model to Animate Diverse Skeletons https://arxiv.org/abs/2609.05415 https://arxiv.org/pdf/2609.05415 https://arxiv.org/html/2609.05415 ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-10-07 - First published: 2026-09-27 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Linzhan/UniMate on Hugging Face](https://huggingface.co/Linzhan/UniMate) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/d550a449-8fde-4be7-9fe9-9c24171348d3) - [Its page on Mindket](https://mindket.com/p/d550a449-8fde-4be7-9fe9-9c24171348d3)