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mohantesting/video-quality-scored

Image-to-Video Quality-Scored Clips A collection of prompted image-to-video samples with quality-evaluation metadata. Each sample pairs a first frame (the I2V conditioning image) with one or both of: a generated video produced by a video model from the first frame + prompt an original clip (the reference/source video the prompt was authored around) A subset of the samples also carry per-clip quality scores: an overall quality_score, six per-aspect breakdowns… See the full description on the dataset page: https://huggingface.co/datasets/mohantesting/video-quality-scored.

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Image-to-Video Quality-Scored Clips

A collection of prompted image-to-video samples with quality-evaluation metadata. Each sample pairs a first frame (the I2V conditioning image) with one or both of:

—a generated video produced by a video model from the first frame + prompt
—an original clip (the reference/source video the prompt was authored around)

A subset of the samples also carry per-clip quality scores: an overall quality_score, six per-aspect breakdowns (first_frame_fidelity, prompt_adherence, temporal_consistency, motion_quality, visual_quality, camera_composition), and an array of multi-evaluator critiques (evaluations) — free-form reasoning paragraphs paired with structured score breakdowns.

Structure

.
├── metadata/
│   ├── slice_400.jsonl       (smallest preset — 400 rows)
│   ├── slice_500.jsonl
│   ├── slice_600.jsonl
│   ├── slice_700.jsonl
│   ├── slice_1000.jsonl
│   ├── slice_2000.jsonl
│   └── slice_4000.jsonl      (full superset — DEFAULT)
└── media/
    ├── sample_NNNNNN_first_frame.png       (conditioning image; always present)
    ├── sample_NNNNNN_generated.mp4         (model output; present when available)
    └── sample_NNNNNN_original_clip.mp4     (reference clip; present when available)

All seven slice metadata files share the same media/ directory. Slice budgets are nested: slice_400 ⊂ slice_500 ⊂ ... ⊂ slice_4000. A given sample_id refers to the same underlying clip across every slice it appears in.

Loading

python

from datasets import load_dataset

# Default (slice_4000 — full superset, 4000 rows)
ds = load_dataset("mohantesting/video-quality-scored")

# Pick a smaller preset
ds_small = load_dataset("mohantesting/video-quality-scored", "slice_500")
ds_mid   = load_dataset("mohantesting/video-quality-scored", "slice_1000")

To resolve a media path:

python

from pathlib import Path
row = ds["train"][0]
first_frame = Path(ds.cache_files[0]["filename"]).parent / row["first_frame"]

Schema

Each row of slice_*.jsonl:

Field	Type	Notes
`sample_id`	`str`	Zero-padded 6-digit string. Stable across slices.
`prompt`	`str`	Natural-language description of the desired video.
`first_frame`	`str`	Relative path to the conditioning image.
`generated_video`	`str \	null`	Relative path to the model-generated clip. Null when no generation is available.
`original_clip`	`str \	null`	Relative path to the reference/source clip.
`quality_score`	`float \	null`	Aggregate quality score (0–100), null when no evaluation was captured.
`first_frame_fidelity`	`float \	null`	Per-aspect score.
`prompt_adherence`	`float \	null`	Per-aspect score.
`temporal_consistency`	`float \	null`	Per-aspect score.
`motion_quality`	`float \	null`	Per-aspect score.
`visual_quality`	`float \	null`	Per-aspect score.
`camera_composition`	`float \	null`	Per-aspect score.
`passed_quality_threshold`	`bool`	`quality_score >= 80.0`. False when score is null.
`evaluations`	`list`	Per-evaluator critiques (see below). May be empty.

Each entry of evaluations:

Field	Type	Notes
`evaluator_id`	`str`	Anonymous identifier (`eval_0`, `eval_1`, ...). Stable within the row.
`passed`	`bool`	Whether the evaluator marked this clip as passing.
`confidence`	`int`	Evaluator self-reported confidence (0–100).
`evaluated_at`	`str`	ISO 8601 timestamp.
`reasoning`	`str`	Free-form rationale (typically 1–3 sentences).
`aspect_scores`	`dict`	Six per-aspect scores (same keys as the row-level breakdown).
`overall_score`	`float`	This evaluator's overall score.

Slice statistics

Slice	Rows	Has generated_video	Has quality_score
`slice_400`	400	342	342
`slice_500`	500	424	424
`slice_600`	600	517	517
`slice_700`	700	603	603
`slice_1000`	1000	850	850
`slice_2000`	2000	1679	1679
`slice_4000`	4000	3372	3372

Notes

—Resolution & length. Most clips are ~832×480 at ~5 seconds. Some samples are slightly off-spec. Re-encode or resize as needed.
—Quality scores range 0–100; the convention used here treats 80 as a practical "passing" threshold, reflected by passed_quality_threshold.
—Multi-evaluator coverage. Rows with evaluations typically carry 3–4 independent critiques. The aggregate quality_score and aspect scores are computed from these (mean over evaluators).
—Reference vs generation. Many samples carry both an original_clip (reference truth) and a generated_video (model output) — useful for comparative/reward-model training. A subset carries only the reference clip.

License

CC-BY-4.0 — please cite this dataset if you find it useful.