---
license: cc-by-4.0
task_categories:
- video-classification
- image-to-video
language:
- en
tags:
- i2v
- image-to-video
- human-preferences
- rlhf
- dpo
- video-generation
- preference-data
- pika
- cogvideox
- video
- text
pretty_name: I2V Human Preferences (Medium)
size_categories:
- 1K<n<10K
dataset_info:
  features:
  - name: prompt_id
    dtype: string
  - name: prompt
    dtype: string
  - name: reference_image
    dtype: image
  - name: pika_video
    dtype: video
  - name: cog_video
    dtype: video
  - name: winner
    dtype: string
  - name: pika_votes
    dtype: int32
  - name: cog_votes
    dtype: int32
  - name: pika_win_rate
    dtype: float32
  - name: cog_win_rate
    dtype: float32
  - name: agreement
    dtype: float32
  - name: detailed_results
    dtype: string
  splits:
  - name: train
    num_bytes: 1897000000
    num_examples: 2000
  download_size: 1897000000
  dataset_size: 1897000000
---

# I2V Human Preferences (Medium)

Human preference dataset for image-to-video (I2V) generation quality. Each row contains a reference image, two generated videos (one from **Pika** and one from **CogVideoX**), and **10 human preference annotations** aggregated via majority vote.

This is the **medium** (2,000-row) subset. See also: [small](https://huggingface.co/datasets/datapointai/image-2-video-human-preferences-medium) (1,000 rows), [large](https://huggingface.co/datasets/datapointai/image-2-video-human-preferences-large) (3,000 rows).

## Dataset Summary

| Metric | Value |
|--------|-------|
| **Total rows** | 2,000 |
| **Annotations per row** | 10 |
| **Total annotations** | 20,000 |
| **Unique prompts** | 2,000 |
| **Models compared** | Pika vs CogVideoX |
| **Media format** | MP4 (video), JPG (reference image) |

## Key Findings

| Metric | Value |
|--------|-------|
| **Pika wins** | 945 (47.2%) |
| **CogVideoX wins** | 703 (35.1%) |
| **Ties** | 352 (17.6%) |
| **Pika overall win rate** | 53.0% |
| **CogVideoX overall win rate** | 47.0% |
| **Mean annotator agreement** | 67.0% |

Pika is preferred overall but the contest is fairly close. The 18% tie rate (5v5 splits across 10 annotators) indicates genuine ambiguity on many prompts.

## Task Setup

Each datapoint consists of:

1. A **reference image** (the input to both I2V models)
2. A **text prompt** describing the desired video
3. **Two candidate videos** — one generated by Pika, one by CogVideoX

Annotators were shown the reference image and both videos side by side and asked to choose which video better represents the prompt given the reference image. Display order was randomized to prevent position bias.

## Dataset Structure

### Columns

| Column | Type | Description |
|--------|------|-------------|
| `prompt_id` | string | UUID linking the reference image and both candidate videos |
| `prompt` | string | Text prompt used for video generation |
| `reference_image` | Image | JPG reference image (input to both models) |
| `pika_video` | Video | MP4 video generated by Pika |
| `cog_video` | Video | MP4 video generated by CogVideoX |
| `winner` | string | Majority-vote label: `pika`, `cog`, or `tie` |
| `pika_votes` | int | Number of annotators who preferred Pika (out of 10) |
| `cog_votes` | int | Number of annotators who preferred CogVideoX (out of 10) |
| `pika_win_rate` | float | Fraction of annotators who preferred Pika |
| `cog_win_rate` | float | Fraction of annotators who preferred CogVideoX |
| `agreement` | float | Inter-annotator agreement (fraction that voted with majority) |
| `detailed_results` | string (JSON) | Per-annotator votes with `winner` and `time_taken_ms` |

### Detailed Results Structure

Each entry in the `detailed_results` JSON array:

```json
{
  "winner": "pika",
  "time_taken_ms": 16411
}
```

## Usage

```python
from datasets import load_dataset

ds = load_dataset("datapointai/image-2-video-human-preferences-medium", split="train")
row = ds[0]
print(row["prompt"])
print(f"Winner: {row['winner']}")
print(f"Pika: {row['pika_votes']}/10, CogVideoX: {row['cog_votes']}/10")
```

### Filter by Agreement

```python
# High-agreement subset (≥80% annotator consensus)
high_agreement = ds.filter(lambda x: x["agreement"] >= 0.8)
print(f"{len(high_agreement)} rows with ≥80% agreement")
```

### Train a Reward Model

```python
from datasets import load_dataset

ds = load_dataset("datapointai/image-2-video-human-preferences-medium", split="train")

for row in ds:
    prompt = row["prompt"]
    reference = row["reference_image"]
    winner = row["winner"]
    pika_score = row["pika_win_rate"]
    cog_score = row["cog_win_rate"]
    # Use as preference pairs for DPO, reward modeling, etc.
    # winner == "pika" → pika_video is preferred over cog_video
    # winner == "cog"  → cog_video is preferred over pika_video
    # winner == "tie"  → no clear preference (consider filtering)
```

### Explore Ties

```python
# Analyze the most ambiguous comparisons
ties = ds.filter(lambda x: x["winner"] == "tie")
print(f"{len(ties)} ties ({len(ties)/len(ds)*100:.1f}%)")
```

## Data Collection

Annotations were collected through [Datapoint AI](https://trydatapoint.com)'s consumer app SDK using forced-choice pairwise comparison. Each comparison was annotated by **10 unique annotators**. Annotators were shown the reference image alongside both candidate videos and asked which video better represents the prompt. Video display positions (left/right) were randomized per session to mitigate position bias.

### Annotation Quality

- **Majority vote**: The `winner` field reflects the choice of >50% of annotators (or `tie` at exactly 50/50)
- **Agreement scores**: The `agreement` field measures consensus — higher values indicate stronger annotator agreement
- **Time tracking**: `time_taken_ms` in detailed results allows filtering out suspiciously fast annotations

## License

CC-BY-4.0

## Citation

```bibtex
@dataset{datapointai_i2v_preferences_2026,
  title={I2V Human Preferences: Pika vs CogVideoX},
  author={Datapoint AI},
  year={2026},
  url={https://huggingface.co/datasets/datapointai/image-2-video-human-preferences-medium},
  note={20,000 pairwise human preference labels for image-to-video generation quality}
}
```