Sci-VBench/Sci-VBench-Videos

Sci-VBench Videos

Sci-VBench Videos is the complete set of model outputs behind the Sci-VBench paper: 11,216 videos from 16 text-to-video models, together with the automatic and human scores computed on them. Every video was generated from the verbatim benchmark prompt under the model's default configuration — no rewriting, no prompt expansion — so the released prompts and the released videos correspond exactly.

Prompts and evaluation specifications live in the companion repo, Sci-VBench/Sci-VBench. A video joins to its prompt and rubrics on video_id alone.

Coverage

Open-source models were run on the full benchmark; proprietary models on the 150-example testmini split.

Model	Access	Videos		Model	Access	Videos
CogVideoX1.5-5B	open	1,253		Gemini-Omni-Flash	API	144
Cosmos3-Nano	open	1,253		HappyHorse-1.1	API	150
HunyuanVideo-1.5	open	1,253		Kling-2.6	API	150
LTX-2	open	1,253		Seedance-2.0	API	148
LTX-2.3	open	1,253		Sora-2	API	150
LongCat-Video	open	1,253		Veo-3.1	API	150
MiniMax-H3	open	1,253		Veo-3.1-Fast	API	150
Wan2.2-5B	open	1,253		Wan-2.6	API	150
				Total		11,216

Gemini-Omni-Flash is 144/150 and Seedance-2.0 is 148/150: the missing prompts were refused by the provider's input filter, identically on every retry. Six of the eight are wet-chemistry procedures involving a strong acid, a heavy-metal reagent, or heating; the other two — an immunofluorescence staining protocol and a golf putt — have no evident trigger. Because prompts are never rephrased in this benchmark, those examples have no path through those models, and the affected model averages are computed over the videos that were produced.

Layout

videos/<video_id>/<Model>.mp4        11,216 videos, 32.5 GB
settings/<video_id>/<Model>.json      5,310 generator sidecars
index.jsonl                           one row per video
judge_scores.jsonl                    MLLM-as-Judge, 3 rounds per video
vt_scores.jsonl                       VBench Video Quality metrics
human_scores.jsonl                    expert ratings, testmini only

Score files

All three key on (video_id, model) and carry nothing but numbers — no reasoning traces, no free-text justifications. The four rubric dimensions are pg (Prompt Grounding), scc (Scientific and Causal Correctness), sc (Spatiotemporal Consistency), and lpf (Low-level Perceptual Fidelity), each a 1–5 integer.

judge_scores.jsonl — 33,648 rows

Rubric-conditioned MLLM-as-Judge (Qwen3.5-397B-A17B), scoring one dimension at a time against that dimension's anchored rubric. Every video is scored in three independent rounds; take the mean over rounds.

Field	Type	Description
video_id	string	Joins to id in the dataset repo
model	string	Generator that produced the video
round	int	Judge run, 1–3
pg scc sc lpf	int	1–5 score for each dimension, or null

Two of the 134,592 score cells are null, where the judge's response could not be parsed on that round: video c248988dbb77aff1e98f4bc0cbef7a62465f7f28 for HunyuanVideo-1.5 (scc, round 2) and for Wan2.2-5B (pg, round 2). Neither is in testmini, and no video loses a dimension entirely, so averaging over the remaining rounds absorbs both — but guard for None when you aggregate.

vt_scores.jsonl — 11,216 rows

The six VBench Video Quality metrics and their unweighted mean, VT, which the paper uses as the automatic proxy for LPF.

Field	Type	Description
video_id model	string	Join key
subject_consistency background_consistency motion_smoothness dynamic_degree aesthetic_quality imaging_quality	float	Individual VBench metrics
VT	float	Unweighted mean of the six

human_scores.jsonl — 2,392 rows

Expert ratings on testmini, one per video, by a domain annotator who did not author the example and who worked from the prompt, the video, and the released evaluation specification. These are the reference labels behind the paper's human evaluation.

Field	Type	Description
video_id model	string	Join key
pg scc sc lpf	int	1–5 expert score for each dimension

index.jsonl — 11,216 rows

One row per video: video_id, model, path, settings (path, or null), testmini, and bytes.

Generation settings

settings/<video_id>/<Model>.json records what actually ran for that video — resolution, frame count, fps, sampler steps, guidance scale, seed, and for locally run models the GPU type and wall time.

Sidecars exist for six of the sixteen models: CogVideoX1.5-5B, Cosmos3-Nano, LTX-2.3 and MiniMax-H3 (1,253 each), plus HappyHorse-1.1 and Seedance-2.0, the two API providers that return their parameters. The other four models we ran ourselves — HunyuanVideo-1.5, LTX-2, LongCat-Video, Wan2.2-5B — predate the sidecar format and have none, so index.settings is null for 5,906 of the 11,216 rows. Settings for every model are tabulated in the paper. Job identifiers have been removed from the sidecars; hardware and timing fields are kept.

Quick start

Download one model rather than all 32.5 GB:

from huggingface_hub import snapshot_download

snapshot_download(
    "Sci-VBench/Sci-VBench-Videos", repo_type="dataset",
    allow_patterns=["videos/*/Sora-2.mp4", "*.jsonl"],
)

Reproduce a model's automatic scores by averaging the three judge rounds. Restrict to testmini for the cross-model comparison; drop the filter to get full-benchmark numbers for an open-source model.

import json, collections, statistics

mini = {json.loads(l)["video_id"] for l in open("index.jsonl") if json.loads(l)["testmini"]}

per_video = collections.defaultdict(lambda: collections.defaultdict(list))
for l in open("judge_scores.jsonl"):
    r = json.loads(l)
    for d in ("pg", "scc", "sc", "lpf"):
        if r[d] is not None:            # two rounds failed to parse; see below
            per_video[(r["model"], r["video_id"])][d].append(r[d])

def score(model, dim, testmini_only=True):
    vals = [statistics.mean(v[dim]) for (m, vid), v in per_video.items()
            if m == model and (not testmini_only or vid in mini)]
    return round(statistics.mean(vals), 2)

for d in ("sc", "pg", "scc"):
    print(d, score("Sora-2", d), score("MiniMax-H3", d, testmini_only=False))

The official evaluation code expects a model-major tree (<dir>/<model>/<id>.mp4). To reshape this repo's id-major layout without copying:

import os, pathlib

src, dst = pathlib.Path("videos"), pathlib.Path("all-videos")
for mp4 in src.glob("*/*.mp4"):
    out = dst / mp4.stem / f"{mp4.parent.name}.mp4"
    out.parent.mkdir(parents=True, exist_ok=True)
    if not out.exists():
        os.link(mp4, out)

License

Released under CC BY 4.0. Outputs of proprietary systems remain subject to their originating provider's terms of service; consult those before redistributing or using them commercially.