Datasets:

Kevinu0628
/

MRBench

---
license: apache-2.0
pretty_name: MRBench
language:
  - en
size_categories:
  - 1K<n<10K
tags:
  - text-motion retrieval
---
# MRBench

MRBench is a benchmark for evaluating motion–text understanding and retrieval
at multiple levels of textual detail. It contains **3,390** motion–text pairs
with consecutive sample IDs from `0` to `3389`.

Every sample provides:

- three text descriptions: concise, standard, and fine-grained;
- a raw motion representation;
- global positions for 22 joints;
- a 263-dimensional motion representation.

Files with the same ID correspond to the same sample across all directories.

## Dataset structure

```text
MRBench/
├── README.md
├── texts/
│   ├── 0.json
│   └── ...
├── raw_feats/
│   ├── 0.pt
│   └── ...
├── joints/
│   ├── 0.pt
│   └── ...
└── guoh3dfeats/
    ├── 0.pt
    └── ...
```

Each data directory contains exactly 3,390 files.

## Text annotations

`texts/{id}.json` contains three description granularities:

```json
{
  "id": 12,
  "concise": "A concise motion description.",
  "standard": "A standard motion description.",
  "fine_grained": "A detailed motion description."
}
```

- `concise` gives a short summary of the primary action.
- `standard` describes the main motion in a complete sentence.
- `fine_grained` describes the temporal sequence, body parts, and motion details.

## Motion representations

### Raw features

`raw_feats/{id}.pt` contains one of two representations:

- `[T, 276]`: ViMoGen-format motion features.
- `[T, 22, 3]`: global joint positions for HY-Motion samples generated with
  Hunyuan3D. These samples do not have a 276-dimensional representation.

The raw representations use a Z-up coordinate convention. A 276-dimensional
file may be stored directly as a `torch.Tensor`, or as a dictionary whose
`motion` field contains the tensor.

The 276-dimensional layout is:

```text
0:126     body pose in 6D rotation representation
126:192   22 joint positions
192:258   22 joint velocities
258:264   root rotation in 6D representation
264:270   root rotation velocity
270:273   root translation
273:276   root translation velocity
```

### Global joints

`joints/{id}.pt` is a `torch.float32` tensor with shape `[T, 22, 3]`. The
coordinates are Y-up and use the T2M/HumanML3D 22-joint skeleton order.

### Guo features

`guoh3dfeats/{id}.pt` is a `torch.float32` tensor with shape `[T-1, 263]`.
These features follow the T2M/HumanML3D preprocessing convention, including its
X-axis flip.

## Loading a sample

```python
import json
from pathlib import Path
import torch
root = Path("MRBench")
sample_id = 0
with (root / "texts" / f"{sample_id}.json").open() as f:
    text = json.load(f)
raw_object = torch.load(
    root / "raw_feats" / f"{sample_id}.pt",
    map_location="cpu",
    weights_only=False,
)
raw_motion = raw_object["motion"] if isinstance(raw_object, dict) else raw_object
joints = torch.load(
    root / "joints" / f"{sample_id}.pt",
    map_location="cpu",
    weights_only=False,
)
guoh3dfeats = torch.load(
    root / "guoh3dfeats" / f"{sample_id}.pt",
    map_location="cpu",
    weights_only=False,
)
```

The loaded values have the following forms:

```text
text["concise"]       string
text["standard"]      string
text["fine_grained"]  string
raw_motion            [T, 276] or [T, 22, 3]
joints                [T, 22, 3]
guoh3dfeats           [T-1, 263]
```

## Typical evaluation

For text-to-motion or motion-to-text retrieval, use one annotation granularity
at a time and report results separately for `concise`, `standard`, and
`fine_grained`. This makes it possible to measure how retrieval performance
changes as textual detail increases.

	---
	license: apache-2.0
	pretty_name: MRBench
	language:
	- en
	size_categories:
	- 1K<n<10K
	tags:
	- text-motion retrieval
	---

	# MRBench

	MRBench is a benchmark for evaluating motion–text understanding and retrieval
	at multiple levels of textual detail. It contains 3,390 motion–text pairs
	with consecutive sample IDs from `0` to `3389`.

	Every sample provides:

	- three text descriptions: concise, standard, and fine-grained;
	- a raw motion representation;
	- global positions for 22 joints;
	- a 263-dimensional motion representation.

	Files with the same ID correspond to the same sample across all directories.

	## Dataset structure

	```text
	MRBench/
	├── README.md
	├── texts/
	│ ├── 0.json
	│ └── ...
	├── raw_feats/
	│ ├── 0.pt
	│ └── ...
	├── joints/
	│ ├── 0.pt
	│ └── ...
	└── guoh3dfeats/
	├── 0.pt
	└── ...
	```

	Each data directory contains exactly 3,390 files.

	## Text annotations

	`texts/{id}.json` contains three description granularities:

	```json
	{
	"id": 12,
	"concise": "A concise motion description.",
	"standard": "A standard motion description.",
	"fine_grained": "A detailed motion description."
	}
	```

	- `concise` gives a short summary of the primary action.
	- `standard` describes the main motion in a complete sentence.
	- `fine_grained` describes the temporal sequence, body parts, and motion details.

	## Motion representations

	### Raw features

	`raw_feats/{id}.pt` contains one of two representations:

	- `[T, 276]`: ViMoGen-format motion features.
	- `[T, 22, 3]`: global joint positions for HY-Motion samples generated with
	Hunyuan3D. These samples do not have a 276-dimensional representation.

	The raw representations use a Z-up coordinate convention. A 276-dimensional
	file may be stored directly as a `torch.Tensor`, or as a dictionary whose
	`motion` field contains the tensor.

	The 276-dimensional layout is:

	```text
	0:126 body pose in 6D rotation representation
	126:192 22 joint positions
	192:258 22 joint velocities
	258:264 root rotation in 6D representation
	264:270 root rotation velocity
	270:273 root translation
	273:276 root translation velocity
	```

	### Global joints

	`joints/{id}.pt` is a `torch.float32` tensor with shape `[T, 22, 3]`. The
	coordinates are Y-up and use the T2M/HumanML3D 22-joint skeleton order.

	### Guo features

	`guoh3dfeats/{id}.pt` is a `torch.float32` tensor with shape `[T-1, 263]`.
	These features follow the T2M/HumanML3D preprocessing convention, including its
	X-axis flip.

	## Loading a sample

	```python
	import json
	from pathlib import Path

	import torch

	root = Path("MRBench")
	sample_id = 0

	with (root / "texts" / f"{sample_id}.json").open() as f:
	text = json.load(f)

	raw_object = torch.load(
	root / "raw_feats" / f"{sample_id}.pt",
	map_location="cpu",
	weights_only=False,
	)
	raw_motion = raw_object["motion"] if isinstance(raw_object, dict) else raw_object

	joints = torch.load(
	root / "joints" / f"{sample_id}.pt",
	map_location="cpu",
	weights_only=False,
	)
	guoh3dfeats = torch.load(
	root / "guoh3dfeats" / f"{sample_id}.pt",
	map_location="cpu",
	weights_only=False,
	)
	```

	The loaded values have the following forms:

	```text
	text["concise"] string
	text["standard"] string
	text["fine_grained"] string
	raw_motion [T, 276] or [T, 22, 3]
	joints [T, 22, 3]
	guoh3dfeats [T-1, 263]
	```

	## Typical evaluation

	For text-to-motion or motion-to-text retrieval, use one annotation granularity
	at a time and report results separately for `concise`, `standard`, and
	`fine_grained`. This makes it possible to measure how retrieval performance
	changes as textual detail increases.