Datasets:

pkulium
/

omini_time_space_eval

license: other
task_categories:
  - text-to-video
tags:
  - text-to-video
  - video-generation
  - benchmark
  - evaluation
  - world-model
size_categories:
  - n<1K

omini_time_space_eval

Text-to-video benchmark suite probing whether a video world model represents time, space, and camera control — 659 generated clips across four benchmarks, with the prompts that produced them and the eval harness that scores them.

Bench	Items	What it tests
`bench_t2v_time_comprehensive`	200	Time/weather transitions (20 variants incl. non-monotonic day→night→day) over outdoor scenes
`bench_t2v_space`	162	Grounded knowledge of specific real-world places, each anchored to a real Wikimedia reference photo (103 worldwide + 59 USA)
`bench_t2v_space_comprehensive`	137	Same space axis, hand-curated from 97 countries, no reference photo
`bench_t2v_camera_time`	160	Instructed camera motion (8 types) crossed with time/weather transition (20 variants) — full 8×20 grid, no confounds

Layout

bench_t2v_camera_time/
  manifest.json        prompts + per-item metadata
  results.jsonl        generation status/timing per item
  videos/*.mp4         the generated clips
  score_claude.py      the eval judge
  summarize.py         aggregation
bench_t2v_space/
  manifest_worldwide.json, manifest_usa.json
  {worldwide,usa}/results.jsonl
  {worldwide,usa}/videos/*.mp4
  score_claude.py, summarize.py
...

Note the eval scripts' default --manifest / --videos-dir values point at the layout of the original working tree (outputs/videos/), not this repo's flattened layout — pass both flags explicitly when running against these files.

Evaluation

score_claude.py is the only judge: it samples 8 frames evenly across each clip, downscales them to 768px on the long edge, and sends them to Claude Opus 5 on Amazon Bedrock with a per-bench rubric (each axis scored 0–10).

Bench	Axes	Pass criteria
`bench_t2v_camera_time`	`time_alignment`, `camera_motion`, `quality`, `smoothness`	≥8, ≥6, ≥7
`bench_t2v_space`, `bench_t2v_space_comprehensive`	`alignment`, `quality`, `smoothness`	≥8, ≥7
`bench_t2v_time_comprehensive`	`time_alignment`, `quality`, `smoothness`	≥8, ≥7

pip install 'anthropic[bedrock]'
export AWS_REGION=us-east-1     # region where Claude Opus 5 is enabled

python score_claude.py --manifest manifest.json --videos-dir videos --out-dir .
python summarize.py --out-dir .

score_claude.py is resumable (skips ids already in claude_scores.jsonl) and concurrent (--concurrency, default 4). summarize.py writes scores.jsonl and summary.json.

Score files are not yet included in this release — the harness is published here, the results will be added in a later revision.

Provenance and licensing

Please read before reuse:

Videos are generated by an internal Cosmos3-Nano checkpoint served through a modified vLLM; they are model outputs, not captured footage.
Prompts in the time/camera benches derive scene descriptions from PAI-Bench-G captions (t2v_prompts.json, outdoor subset). The image_name field carries the source identifier. PAI-Bench-G's own license governs reuse of that derived text.
ref_image paths in the space manifests point at Wikimedia Commons photos that are not redistributed here — only the relative filename and the source_url are included. Each such photo carries its own CC license; fetch and attribute individually if you need them.
Clips depict real landmarks. They are synthetic generations, not photographs of those places.

The license: other tag reflects that the components above carry different terms; there is no single blanket license for this repository.