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A jogger in bright athletic wear marches in rhythm through a neon-lit alley in Hong Kong at night, the vibrant colors reflecting off the wet pavement as the hum of distant voices and the scent of street food fill the air.
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A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
veo3_fast
prompt_adherence
veo3_fast
right
14,429
c67b77afcbde
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
veo3_fast
aesthetic
veo3_fast
left
8,235
e25c23e68b73
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
kling
coherence
kling
left
21,363
aa74265922f5
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
veo3_fast
aesthetic
veo3_fast
left
24,506
063a55363b3c
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
veo3_fast
prompt_adherence
veo3_fast
left
14,378
8a52cb1e8da3
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
kling
aesthetic
kling
left
17,388
8f45697f4517
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
veo3_fast
aesthetic
veo3_fast
left
13,900
59c2b52bc33d
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
kling
aesthetic
luma_ray2
right
35,451
0fd5f8206faf
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
veo3_fast
coherence
luma_ray2
left
11,321
332a7b25aaae
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
kling
aesthetic
kling
left
14,909
3e983942d97f
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
kling
coherence
luma_ray2
right
13,719
1690c8431987
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
veo3_fast
prompt_adherence
luma_ray2
left
12,921
9a11dd7b02e5
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
veo3_fast
prompt_adherence
veo3_fast
left
59,142
365385251255
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
kling
coherence
kling
right
15,685
493d2814213c
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
kling
aesthetic
kling
right
12,341
0dbb08e37daa
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
grok
coherence
grok
left
13,352
3350dea7856f
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
kling
aesthetic
kling
left
13,634
1dd65941601f
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
veo3_fast
coherence
veo3_fast
right
12,962
3eb342a17610
7
A businessman in a tailored navy suit strides briskly across a fog-shrouded suspension bridge, his polished shoes echoing against the steel cables. The mist clings to the air, wrapping around him as he clutches his leather briefcase tightly, determination etched on his face.
walking
luma_ray2
veo3_fast
prompt_adherence
luma_ray2
left
13,571
56a33442c8e1
End of preview. Expand in Data Studio

VidPrefMotion: Human Preference Data for Video Generation Models

A large-scale human evaluation of AI-generated human motion videos

29,283 pairwise preference labels from 4,349 annotators comparing four frontier video generation models across six human motion categories and three evaluation dimensions.


Overview

Stat Value
Total preference labels 29,283
Unique annotators 4,349
Video generation models 4 (Luma Ray 2, Veo 3 Fast, Kling 1.5 Pro, Grok Imagine)
Unique prompts 60
Videos generated 238
Pairwise comparisons 1,062
Evaluation dimensions 3 (Coherence, Aesthetic, Prompt Adherence)
Motion subcategories 6 (Walking, Dancing, Talking, Sports, Stationary, Multi-Person)
Labels per comparison ~28 (median)
Median response time 14.9 seconds

Why This Dataset Exists

Video generation models are advancing rapidly, but evaluation remains an open problem. Automated judges (VLMs) are fast and cheap but unreliable — especially on human motion, where subtle errors in gait, facial expressions, and multi-body coordination are hard to detect programmatically.

This dataset provides ground-truth human preferences specifically for human motion videos, broken down by the dimensions that matter most to video quality: temporal coherence, visual aesthetics, and prompt faithfulness.


Dataset Structure

Each row represents a single annotator's preference between two AI-generated videos.

Fields

Field Type Description
prompt_id int Unique prompt identifier (1-60)
prompt string The text prompt used to generate both videos
subcategory string Human motion type: walking, dancing, talking, sports, stationary, multi_person
model_a string Model that generated video 1
model_b string Model that generated video 2
dimension_id string Evaluation dimension: coherence, aesthetic, prompt_adherence
choice string Annotator's preference: video_1 (model_a) or video_2 (model_b)
display_order list Randomized left/right presentation order
time_taken_ms int Annotator response time in milliseconds

Dimension Descriptions

  • Coherence: Is the video temporally consistent? No flickering, warping, deformation, or physically implausible motion.
  • Aesthetic: Is the video visually appealing? Composition, lighting, color, style, and production quality.
  • Prompt Adherence: Does the video accurately depict the prompt? Object presence, attributes, actions.

Motion Subcategories

Subcategory Examples Why it's hard
Walking/Running Gaits, jogging, sprinting Weight shift, foot contact, natural rhythm
Dancing Ballet, hip-hop, folk Complex coordinated movement, full-body
Talking/Expressions Speaking, singing, laughing Lip sync, facial micro-movements
Sports/Action Martial arts, skateboarding, swimming Fast motion, physics, athletic poses
Stationary Meditating, reading, posing Subtle motion, identity preservation
Multi-Person Handshakes, sparring, performing Two+ bodies, occlusion, interaction physics

Key Results

Model Rankings (Overall Human Preference Win Rate)

Rank Model Win Rate 95% CI
1 Grok Imagine 54.7% [54.0%, 55.5%]
2 Veo 3 Fast 54.6% [53.8%, 55.3%]
3 Kling 1.5 Pro 47.9% [47.1%, 48.7%]
4 Luma Ray 2 42.8% [42.0%, 43.6%]

Rankings by Dimension

Model Coherence Aesthetic Prompt Adherence
Grok Imagine 53.6% 55.7% 54.7%
Veo 3 Fast 54.5% 54.7% 54.5%
Kling 1.5 Pro 48.4% 48.0% 47.4%
Luma Ray 2 43.5% 41.5% 43.5%

Data Quality

Position Bias Control

Videos were randomly shuffled between left and right positions for each comparison. Observed selection rate: 52.8% left / 47.2% right — near the 50/50 baseline, confirming minimal position bias.

Response Time

Median annotator response time: 14.9 seconds, indicating genuine engagement with the video content (each video is 4-5 seconds, so annotators watched both videos at least once before deciding).

Annotator Scale

4,349 unique annotators with a median of 4 labels each, ensuring diverse perspectives and reducing individual annotator bias.


Methodology

Prompt Generation

60 prompts were generated using a structured diversity approach: subjects, actions, and settings were uniquely combined per subcategory, then refined by GPT-4o-mini into natural 1-2 sentence descriptions (30-50 words each).

Video Generation

Each prompt was submitted to all four models via Fal.ai API (single inference, no cherry-picking). All videos are 4-5 seconds at 540p-720p, 16:9 aspect ratio.

Annotation Pipeline

Labels were collected through a mobile-first annotation interface embedded in a consumer app. Each comparison was presented as a forced-choice between two videos with a dimension-specific question (e.g., "Which video has more coherent and consistent motion?"). Display order was randomized and logged.

Quality Controls

  • Randomized video positions (logged for bias analysis)
  • Response time tracking (median 14.9s confirms engagement)
  • Multiple independent annotators per comparison (~28 labels each)
  • Dawid-Skene aggregation available for consensus estimation

Use Cases

  1. Reward model training — Train preference models for video generation RLHF using real human judgments instead of VLM proxies
  2. Model benchmarking — Compare video generation models on human motion specifically, broken down by motion type and quality dimension
  3. VLM judge calibration — Measure where automated judges agree/disagree with human preferences to identify failure modes
  4. Evaluation research — Study inter-annotator agreement patterns in subjective video quality assessment

Citation

@dataset{vidprefmotion2026,
  title={VidPrefMotion: Human Preference Data for Video Generation Models},
  author={Datapoint AI},
  year={2026},
  url={https://huggingface.co/datasets/datapointai/VidPrefMotion}
}

License

CC-BY-4.0


Contact

For custom evaluation studies, larger-scale labelling, or API access to our annotation pipeline, visit trydatapoint.com.

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