README.md

metadata

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

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:

{
  "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:

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

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["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.