OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for
  Subject-to-Video Generation
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.
68366f692c00148ea4021e48
Shenghai Yuan
claimed_verified
2025-05-28T08:58:07.937Z
63468720dd6d90d82ccf3450
https://cdn-avatars.huggingface.co/v1/production/uploads/63468720dd6d90d82ccf3450/tVBFlmZNz8FRMkOrDaDID.jpeg
YSH
user
BestWishYsh
68366f692c00148ea4021e49
Xianyi He
admin_assigned
2025-05-28T10:13:24.658Z
6688f94517f8d8f81c2def5f
/avatars/1064ccd62539a8ca2ec86fc87c818296.svg
hexianyi
user
pkuhexianyi
68366f692c00148ea4021e4a
Yufan Deng
admin_assigned
2025-05-28T10:13:05.574Z
64210d1fd039a891a914986d
/avatars/b178a768657eb223bdbfbd9e0a2000ff.svg
Yufan Deng
user
dyf
68366f692c00148ea4021e4b
Yang Ye
68366f692c00148ea4021e4c
Jinfa Huang
admin_assigned
2025-05-28T10:12:41.714Z
63f37af60be81bdc5d92eebb
/avatars/b8dfdff4ab36988ec9a8643e82a3d2db.svg
Huang
user
Jinfa
68366f692c00148ea4021e4d
Bin Lin
claimed_verified
2025-06-04T09:04:17.095Z
6367a8175bb06007ea099b8f
https://cdn-avatars.huggingface.co/v1/production/uploads/6367a8175bb06007ea099b8f/IjG7HyWyWRlVt_XwRbxRW.jpeg
linbin
user
LanguageBind
68366f692c00148ea4021e4e
Chongyang Ma
admin_assigned
2025-05-28T10:13:32.853Z
66fedabfa77ec468ff320767
/avatars/5bd869c30df0f71733006081343b1bd0.svg
Chongyang Ma
user
chongyangma
68366f692c00148ea4021e4f
Jiebo Luo
68366f692c00148ea4021e50
Li Yuan
OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for
  Subject-to-Video Generation
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.
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.
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