Open-Domain Subject-to-Video

CoIR (Code Information Retrieval) benchmark, is designed to evaluate code retrieval capabilities. CoIR includes 10 curated code datasets, covering 8 retrieval tasks across 7 domains. In total, it encompasses two million documents. It also provides a common and easy Python framework, installable via pip, and shares the same data schema as benchmarks like MTEB and BEIR for easy cross-benchmark evaluations.

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.

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.