Datasets:

nvidia
/

Kimodo-Motion-Gen-Benchmark

license: cc-by-4.0
language:
  - en
tags:
  - Kimodo
  - nvidia
  - human motion generation
pretty_name: Kimodo Human Motion Generation Benchmark
viewer: false

Kimodo Human Motion Generation Benchmark

Kimodo Codebase, Benchmark Documentation

Dataset Description:

This dataset provides the necessary metadata to construct the suite of test cases that make up the Kimodo human motion generation benchmark. This includes test cases that evaluate text-following for the text-to-motion task, along with constraint-following for constraint-conditioned motion generation.

The benchmark is constructed from the SOMA uniform version of the BONES-SEED dataset using metadata included in this repo, which includes text prompts derived from our SEED timeline annotations, start/end frames for test case motions, and pose constraint configurations. To get started with the benchmark, see the Kimodo documentation.

This repo also includes the train and test splits for the BONES-SEED data that should be used to train models that will be evaluated with the Kimodo benchmark.

This dataset is ready for commercial use.

Dataset Owner:

NVIDIA Corporation

Dataset Creation Date:

April 2026

License/Terms of Use:

This dataset is governed by the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Intended Usage:

This dataset is intended for researchers and developers training motion generation models on BONES-SEED to evaluate their models in a comprehensive and standardized way.

Download Instructions:

The easiest way to download the dataset is using Git:

git clone git@hf.co:datasets/nvidia/Kimodo-Motion-Gen-Benchmark

Dataset Structure:

For full details of the benchmark, please see the Kimodo documentation.

Train and test splits are defined in splits:

train_split_paths.txt - filenames of training data
test_content_split_paths.txt - filenames for test split containing new semantic "content". This split contains motions with content_name (from the BONES-SEED metadata) that are not seen in the training split. This tests model generalization to new semantic motion types, e.g. for text-to-motion generalization.
test_repetition_split_paths.txt - filenames for test split containing new motions from content that was seen in training. This split contains motions where the content_name is contained in the training split, but the exact motion itself was not seen. This tests a model's ability to generalize to novel performances of a familiar motion type, e.g., for constraint-following generalization.

The benchmark test cases are partitioned into the content and repetition test splits. Note that the test cases in the benchmark cover a diverse subset of these splits. Within each of the split directories, the structure categorizes test cases into tasks ranging from pure text-to-motion to constraint-conditioned generation with a variety of different constraint types and scenarios:

testsuite
├── content
│   ├── constraints_notext
│   │   ├── end-effectors
│   │   ├── fullbody
│   │   ├── mixture
│   │   └── root
│   ├── constraints_withtext
│   │   ├── end-effectors
│   │   ├── fullbody
│   │   ├── mixture
│   │   └── root
│   └── text2motion
│       ├── overview
│       ├── timeline_multi
│       └── timeline_single
└── repetition
    ├── constraints_notext
    │   ├── end-effectors
    │   ├── fullbody
    │   ├── mixture
    │   └── root
    ├── constraints_withtext
    │   ├── end-effectors
    │   ├── fullbody
    │   ├── mixture
    │   └── root
    └── text2motion
        ├── overview
        ├── timeline_multi
        └── timeline_single

At the lowest level of this structure, each leaf folder contains indexed test cases (0000, 0001, 0002, ...). For example:

end-effectors/feet_posrot/
├── 0000/
├── 0001/
├── 0002/
...
└── 0255/

Each index folder is one standalone test case with its own meta.json containing text prompt and duration, seed_motion.json with metadata relating the test case to BONES-SEED, and optionally seed_constraints.json defining constraints for the test case. These are used to process the BONES-SEED dataset to build the full benchmark.

All text prompts in the benchmark are derived from our SEED timeline annotations.

Dataset Quantification:

Dataset file size: 116 MB
Total number of test cases: 22,474

References:

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
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