license: cc-by-4.0
task_categories:
- image-to-video
- text-to-video
language:
- en
size_categories:
- n<1K
pretty_name: VBVR-MultiStep-Bench
tags:
- video-reasoning
- multi-step
- long-horizon
- image-to-video
- evaluation
- benchmark
VBVR-MultiStep-Bench
The frozen 180-instance public evaluation split released alongside the VBVR-MultiStep training corpus. Designed for long-horizon multi-step image-to-video (I2V) reasoning evaluation.
This dataset is part of the VBVR (Very Big Video Reasoning Suite) project. See the parent suite at https://video-reason.com and the suite paper VBVR: A Very Big Video Reasoning Suite (Wang et al., ICML 2026).
At a glance
| Property | Value |
|---|---|
| Tasks | 36 parameterized tasks (Multi-01 … Multi-36) |
| Reasoning families | Navigation, Planning, CSP, Execution, Geometry, Physics |
| Instances | 180 (5 per task × 36) |
| Per-instance artifacts | 5 (see below) |
| License | CC-BY-4.0 |
Five-artifact data contract
Every instance lives at:
Multi-XX_<name>_data-generator/Multi-XX_<name>_data-generator_task/Multi-XX_<name>_data-generator_<id>/
and contains exactly:
| File | Role |
|---|---|
first_frame.png |
Model conditioning image (the only visual input the model receives at inference) |
prompt.txt |
Natural-language task contract |
final_frame.png |
Target endpoint (held out from the model) |
ground_truth.mp4 |
Reference rollout demonstrating the correct trajectory |
question_metadata.json |
Seed, version, tolerances, task-specific fields |
A top-level metadata.parquet indexes every instance with the task id, family, seed, and per-instance metadata for fast filtering.
Reasoning families
| Family | Characteristic | Released tasks |
|---|---|---|
| Navigation | Discrete motion under adjacency / obstacle constraints | 6 |
| Planning | Operator-based state transformation | 6 |
| CSP | Incremental labeling under global consistency | 6 (3 used for human judging) |
| Execution | Clocked deterministic update rules | 6 |
| Geometry | Ordered constructive geometry | 6 |
| Physics | Continuous dynamics with contact / conservation | 6 |
Tasks Multi-13, Multi-14, Multi-15 (CSP) are excluded from the human-judging pool described in the paper but are included in this release for completeness.
Intended use
- Primary use: trajectory-level evaluation of I2V systems under a fixed five-artifact contract.
- Comparison protocol: blind human pairwise judging on three independent axes — process correctness, reference fidelity, render quality.
- Companion training corpus: Video-Reason/VBVR-MultiStep (~360k samples).
Loading
import pandas as pd
meta = pd.read_parquet("hf://datasets/Video-Reason/VBVR-MultiStep-Bench/metadata.parquet")
Or pull a single instance:
from huggingface_hub import hf_hub_download
prompt_path = hf_hub_download(
"Video-Reason/VBVR-MultiStep-Bench",
"Multi-01_maze_shortest_path_data-generator/Multi-01_maze_shortest_path_data-generator_task/Multi-01_maze_shortest_path_data-generator_00000000/prompt.txt",
repo_type="dataset",
)
License
Released under CC-BY-4.0. The reference rollouts are produced from generators that consume only released task definitions; no third-party copyrighted content is embedded.
Wan2.2-I2V-A14B (Apache-2.0) is referenced as a baseline model and a fine-tuning ancestor for VBVR-Wan2.2; this dataset does not redistribute Wan2.2 weights.
Responsible AI
This dataset is fully synthetic — generators produce every instance from controlled parameters. There are no human subjects, no scraped media, and no personal information. See the Croissant file for the complete RAI metadata.