MolmoPoint-Data
Collection
Data used in the MolmoPoint models • 3 items • Updated • 3
Datasets:
id stringlengths 27 31 | video stringlengths 43 61 | expression stringlengths 7 82 | fps int64 6 6 | sampling_fps int64 2 2 | height int64 512 512 | n_frames int64 120 120 | width int64 512 512 | task stringclasses 1
value | frame_trajectories listlengths 20 120 | mask_id listlengths 2 10 | obj_id listlengths 2 10 | qid stringclasses 4
values | anno_id listlengths 2 10 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
molmopoint-tracksyn_track_0 | static-camera/0_run_20260118_201653/video-20260118_202058 | the red toolboxes | 6 | 2 | 512 | 120 | 512 | track | [
{
"frame": 0,
"time": 0,
"points": [
{
"id": 0,
"point": [
340,
422
],
"occluded": false
},
{
"id": 1,
"point": [
331,
292
],
"occluded": false
},
{
"id": 2,
... | [
"0",
"1",
"2",
"3",
"4"
] | [
1,
3,
6,
7,
8
] | 0 | [
"1",
"3",
"6",
"7",
"8"
] |
molmopoint-tracksyn_track_1 | static-camera/0_run_20260118_201653/video-20260118_202058 | the wooden toolboxes | 6 | 2 | 512 | 120 | 512 | track | [
{
"frame": 0,
"time": 0,
"points": [
{
"id": 0,
"point": [
458,
133
],
"occluded": false
},
{
"id": 2,
"point": [
399,
180
],
"occluded": false
}
]
},
{
"frame": 1,... | [
"0",
"1",
"2"
] | [
2,
5,
10
] | 1 | [
"2",
"5",
"10"
] |
molmopoint-tracksyn_track_2 | static-camera/0_run_20260118_201653/video-20260118_202058 | the open-top wooden toolboxes | 6 | 2 | 512 | 120 | 512 | track | [
{
"frame": 0,
"time": 0,
"points": [
{
"id": 0,
"point": [
458,
133
],
"occluded": false
},
{
"id": 1,
"point": [
399,
180
],
"occluded": false
}
]
},
{
"frame": 1,... | [
"0",
"1"
] | [
2,
10
] | 2 | [
"2",
"10"
] |
molmopoint-tracksyn_track_3 | static-camera/10000_run_20260119_103155/video-20260119_105615 | the T-pose characters | 6 | 2 | 512 | 120 | 512 | track | [
{
"frame": 0,
"time": 0,
"points": [
{
"id": 0,
"point": [
118,
178
],
"occluded": false
},
{
"id": 1,
"point": [
227,
145
],
"occluded": false
},
{
"id": 2,
... | [
"0",
"1",
"2"
] | [
5,
7,
8
] | 0 | [
"5",
"7",
"8"
] |
molmopoint-tracksyn_track_4 | static-camera/10000_run_20260119_103155/video-20260119_105615 | the figures wearing tracksuits | 6 | 2 | 512 | 120 | 512 | track | [
{
"frame": 0,
"time": 0,
"points": [
{
"id": 0,
"point": [
406,
91
],
"occluded": false
},
{
"id": 1,
"point": [
44,
348
],
"occluded": false
}
]
},
{
"frame": 1,
... | [
"0",
"1"
] | [
2,
8
] | 1 | [
"2",
"8"
] |
molmopoint-tracksyn_track_5 | static-camera/10000_run_20260119_103155/video-20260119_105615 | the figures wearing dresses | 6 | 2 | 512 | 120 | 512 | track | [
{
"frame": 0,
"time": 0,
"points": [
{
"id": 0,
"point": [
258,
224
],
"occluded": false
},
{
"id": 1,
"point": [
188,
397
],
"occluded": false
}
]
},
{
"frame": 1,... | [
"0",
"1"
] | [
4,
9
] | 2 | [
"4",
"9"
] |
molmopoint-tracksyn_track_6 | static-camera/10001_run_20260119_105626/video-20260119_110708 | the showers | 6 | 2 | 512 | 120 | 512 | track | [
{
"frame": 0,
"time": 0,
"points": [
{
"id": 0,
"point": [
243,
326
],
"occluded": false
},
{
"id": 1,
"point": [
270,
264
],
"occluded": false
}
]
},
{
"frame": 1,... | [
"0",
"1"
] | [
6,
8
] | 0 | [
"6",
"8"
] |
molmopoint-tracksyn_track_7 | static-camera/10001_run_20260119_105626/video-20260119_110708 | the meat pieces | 6 | 2 | 512 | 120 | 512 | track | [
{
"frame": 0,
"time": 0,
"points": [
{
"id": 0,
"point": [
419,
383
],
"occluded": false
},
{
"id": 2,
"point": [
203,
416
],
"occluded": false
},
{
"id": 3,
... | [
"0",
"1",
"2",
"3",
"4",
"5"
] | [
1,
2,
4,
5,
7,
9
] | 1 | [
"1",
"2",
"4",
"5",
"7",
"9"
] |
molmopoint-tracksyn_track_8 | static-camera/10001_run_20260119_105626/video-20260119_110708 | the raw steaks | 6 | 2 | 512 | 120 | 512 | track | [
{
"frame": 1,
"time": 0.16666666666666602,
"points": [
{
"id": 1,
"point": [
345,
10
],
"occluded": false
}
]
},
{
"frame": 5,
"time": 0.833333333333333,
"points": [
{
"id": 0,
"point": [
... | [
"0",
"1"
] | [
2,
9
] | 2 | [
"2",
"9"
] |
molmopoint-tracksyn_track_9 | static-camera/10002_run_20260119_110719/video-20260119_111711 | the wardrobes | 6 | 2 | 512 | 120 | 512 | track | [{"frame":0,"time":0.0,"points":[{"id":0,"point":[213.0,126.0],"occluded":false},{"id":1,"point":[48(...TRUNCATED) | [
"0",
"1",
"2",
"3",
"4",
"5",
"6",
"7",
"8"
] | [
1,
3,
4,
5,
6,
7,
8,
9,
10
] | 0 | [
"1",
"3",
"4",
"5",
"6",
"7",
"8",
"9",
"10"
] |
End of preview. Expand in Data Studio
MolmoPoint-TrackSyn Dataset
Synthetic point tracking annotations for procedurally generated videos generated with Blender.
Each example contains an expression describing an object, per-frame point trajectories, and video metadata. All videos are encoded as 6 FPS and points are sampled at 2 FPS.
Dataset Statistics
| Video Source | Unique Annotations | Unique Videos |
|---|---|---|
| static-camera | 34,324 | 11,629 |
| dyna-camera | 41,841 | 14,158 |
| Total | 76,165 | 25,787 |
Schema
| Column | Type | Description |
|---|---|---|
id |
string |
Unique example identifier |
video |
string |
Relative video path (without extension), e.g. static-camera/{run_dir}/{video_file}. We support static camera (static-camera) and dynamic camera (dyna-camera) setups. |
expression |
string |
Natural-language description of the tracked object |
fps |
int64 |
Original video FPS |
sampling_fps |
int64 |
Sampling FPS used for annotation (always 2) |
height |
int64 |
Video height in pixels |
width |
int64 |
Video width in pixels |
n_frames |
int64 |
Number of frames in the sampled clip |
task |
string |
Task type (always "track") |
frame_trajectories |
list[object] |
Per-frame point tracks (frame index, timestamp, point coords + occlusion) |
mask_id |
list[string] |
Optional mask identifiers |
obj_id |
list[int64] |
Optional object identifiers |
Video Download
Videos are bundled in this repository as synthetic_tracks.tar.
Automatic download
from olmo.data.molmo2_video_track_datasets import MolmoPointTrackSyn
# Downloads the tar from HF, extracts, and verifies
MolmoPointTrackSyn.download()
Manual download
# Download the tar from HuggingFace
huggingface-cli download allenai/MolmoPoint-TrackSyn synthetic_tracks.tar --repo-type dataset --local-dir ./MolmoPoint-TrackSyn
# Extract
tar -xf ./MolmoPoint-TrackSyn/synthetic_tracks.tar -C ./MolmoPoint-TrackSyn/
After extraction the directory structure is:
MolmoPoint-TrackSyn/
├── static-camera/
│ ├── {run_dir}/
│ │ ├── video.mp4
│ │ └── metadata.json
│ └── ...
└── dyna-camera/
├── {run_dir}/
│ ├── video.mp4
│ └── metadata.json
└── ...
The video column maps directly to the file path: `{VIDEO_HOME}/{video}/video.mp4
Usage
from datasets import load_dataset
# Load the dataset
ds = load_dataset("allenai/MolmoPoint-TrackSyn", split="train")
# Inspect an example
print(ds[0])
Citation
If you use this dataset, please cite the MolmoPoint paper.
License
Dataset license: ODC-BY Dataset card (License section): This dataset is licensed under ODC-BY. It is intended for research and educational use in accordance with Ai2’s Responsible Use Guidelines.
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Size of downloaded dataset files:
63.9 MB
Size of the auto-converted Parquet files:
63.9 MB
Number of rows:
76,165