title: OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for
    Subject-to-Video Generation
summary: Subject-to-Video (S2V) generation aims to create videos that faithfully
  incorporate reference content, providing enhanced flexibility in the production
  of videos. To establish the infrastructure for S2V generation, we propose
  OpenS2V-Nexus, consisting of (i) OpenS2V-Eval, a fine-grained benchmark, and
  (ii) OpenS2V-5M, a million-scale dataset. In contrast to existing S2V
  benchmarks inherited from VBench that focus on global and coarse-grained
  assessment of generated videos, OpenS2V-Eval focuses on the model's ability to
  generate subject-consistent videos with natural subject appearance and identity
  fidelity. For these purposes, 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 and GmeScore, to
  separately quantify subject consistency, naturalness, and text relevance in
  generated videos. Building on this, we conduct a comprehensive evaluation of 16
  representative S2V models, highlighting their strengths and weaknesses across
  different content. Moreover, 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. Specifically, we ensure subject-information
  diversity in our dataset by (1) segmenting subjects and building pairing
  information via cross-video associations and (2) prompting GPT-Image-1 on raw
  frames to synthesize multi-view representations. Through OpenS2V-Nexus, we
  deliver a robust infrastructure to accelerate future S2V generation research.
paper_authors._id: 68366f692c00148ea4021e48
paper_authors.name: Shenghai Yuan
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paper_authors.name: Xianyi He
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paper_authors.name: Yufan Deng
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paper_authors.fullname: Yufan Deng
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paper_authors.name: Yang Ye
paper_authors._id: 68366f692c00148ea4021e4c
paper_authors.name: Jinfa Huang
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paper_authors.fullname: Huang
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paper_authors.name: Bin Lin
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paper_authors.fullname: linbin
paper_authors.type: user
paper_authors.user: LanguageBind
paper_authors._id: 68366f692c00148ea4021e4e
paper_authors.name: Chongyang Ma
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-05-28T10:13:32.853Z
paper_authors._id: 66fedabfa77ec468ff320767
paper_authors.avatarUrl: /avatars/5bd869c30df0f71733006081343b1bd0.svg
paper_authors.fullname: Chongyang Ma
paper_authors.type: user
paper_authors.user: chongyangma
paper_authors._id: 68366f692c00148ea4021e4f
paper_authors.name: Jiebo Luo
paper_authors._id: 68366f692c00148ea4021e50
paper_authors.name: Li Yuan
paper_title: OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for
    Subject-to-Video Generation
paper_summary: Subject-to-Video (S2V) generation aims to create videos that faithfully
  incorporate reference content, providing enhanced flexibility in the production
  of videos. To establish the infrastructure for S2V generation, we propose
  OpenS2V-Nexus, consisting of (i) OpenS2V-Eval, a fine-grained benchmark, and
  (ii) OpenS2V-5M, a million-scale dataset. In contrast to existing S2V
  benchmarks inherited from VBench that focus on global and coarse-grained
  assessment of generated videos, OpenS2V-Eval focuses on the model's ability to
  generate subject-consistent videos with natural subject appearance and identity
  fidelity. For these purposes, 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 and GmeScore, to
  separately quantify subject consistency, naturalness, and text relevance in
  generated videos. Building on this, we conduct a comprehensive evaluation of 16
  representative S2V models, highlighting their strengths and weaknesses across
  different content. Moreover, 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. Specifically, we ensure subject-information
  diversity in our dataset by (1) segmenting subjects and building pairing
  information via cross-video associations and (2) prompting GPT-Image-1 on raw
  frames to synthesize multi-view representations. Through OpenS2V-Nexus, we
  deliver a robust infrastructure to accelerate future S2V generation research.
paper_ai_summary: OpenS2V-Nexus provides benchmarks and a large dataset to evaluate and advance Subject-to-Video (S2V) generation, focusing on subject consistency and naturalness in generated videos.
paper_ai_keywords: Subject-to-Video S2V OpenS2V-Eval OpenS2V-5M VBench fine-grained benchmark subject-consistent videos natural subject appearance identity fidelity NexusScore NaturalScore GmeScore subject consistency naturalness text relevance S2V models multi-view representations GPT-Image-1 cross-video associations