Omkar2118/datasets huggingface dataset:
OpenS2V-5M

We create the first open-source large-scale S2V generation dataset **OpenS2V-5M**, which consists of five million high-quality 
720P subject-text-video triples. To ensure subject-information diversity in our dataset by, we **(1)** segmenting subjects 
and building pairing information via cross-video associations and **(2)** prompting GPT-Image on raw frames to synthesize multi-view representations. `The dataset supports both Subject-to-Video and Text-to-Video generation tasks.`

Introducing paper: OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation
OpenS2V-Eval

**OpenS2V-Eval** introduces 180 prompts from seven major categories of S2V, which incorporate both real and synthetic test data. Furthermore, 
to accurately align human preferences with S2V benchmarks, we propose three automatic metrics: **NexusScore**, **NaturalScore**, **GmeScore**
to separately quantify subject consistency, naturalness, and text relevance in generated videos. Building on this, we conduct a comprehensive 
evaluation of 14 representative S2V models, highlighting their strengths and weaknesses across different content.

Introducing paper: OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation