File size: 2,695 Bytes
f8d2407 3b7ad08 69f84a9 042382f 69f84a9 f8d2407 69f84a9 f8d2407 3b7ad08 addc511 69f84a9 f8d2407 69f84a9 addc511 f8d2407 be4d078 f8d2407 69f84a9 f8d2407 69f84a9 f8d2407 69f84a9 f8d2407 3b7ad08 f8d2407 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
---
language:
- en
license: mit
task_categories:
- text-to-video
- image-to-video
dataset_info:
features:
- name: id
dtype: string
- name: domain
dtype: string
- name: task_type
dtype: string
- name: prompt
dtype: string
- name: image
dtype: image
- name: reference_frames
sequence: image
- name: reference_text
sequence: string
- name: protocol
sequence: string
configs:
- config_name: default
data_files:
- split: train
path: dataset.parquet
---
# Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning
[**Paper**](https://huggingface.co/papers/2512.24952) | [**Code**](https://github.com/RUCAIBox/VIPER)
## 👀 About VIPER
- Overview: Process-aware evaluation for Generative Video Reasoning tasks.
- Statistics: 309 carefully curated samples spanning 6 distinct domains (i.e., temporal, structural, symbolic, spatial, physics and planning reasoning).
- New Metric: Process-outcome Consistency (POC@r). POC@r evaluate video correctness at both process- and outcome-level, with multiple frames uniformly sampled from the whole video at rate r, instead of the last frame only.
<p align="center">
<img src="https://huggingface.co/datasets/Monosail/VIPER/resolve/main/overview.png" width="85%"> <br>
</p>
## Dataset Statistics
### Domain Distribution
| Domain | Total Samples | Task Types |
|--------|---------------|------------|
| Physics | 32 | experiment, game |
| Planning | 44 | navigation, obj_manipulation |
| Spatial | 60 | block_rotate, dice, image_restore |
| Structural | 70 | chess, maze, sudoku, ttt |
| Symbolic | 60 | knowledge, math, multimodal |
| Temporal | 43 | obj_move, zoom |
## 📦 Dataset Usage
### Download
```python
from datasets import load_dataset
# Load the full dataset
dataset = load_dataset("Monosail/VIPER")
```
### Data Fields
- `id`: Unique identifier for the sample
- `domain`: The reasoning domain (Physics, Planning, Spatial, Structural, Symbolic, Temporal)
- `task_type`: Specific task category within the domain
- `prompt`: Text prompt describing the task
- `image`: The input image
- `reference_frames`: Ground-truth image frames
- `reference_texts`: Ground-truth text descriptions
- `protocol`: Process-level task constraints
## 📝 Citation
If you find our benchmark useful, please consider citing us:
```bibtex
@article{li2026viper,
title={Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning},
author={Li, Yifan and Gu, Yukai and Min, Yingqian and Liu, Zikang Mirror and Du, Yifan and Zhou, Kun and Yang, Min and Zhao, Wayne Xin and Qiu, Minghui},
journal={arXiv preprint arXiv:2512.24952},
year={2025}
}
``` |