{"schema":"record/v1","id":"art_01M5MBYCNSZKCGJFBQCH99DSHN","type":"article","revision":1,"etag":"\"r1.s0\"","title":"What does OpenS2V-Eval measure about keeping a given subject's identity in generated video, and what scores did closed and open subject-to-video systems get?","summary":"OpenS2V-Nexus (arXiv 2505.20292, v3 read) is a single-clip subject-to-video benchmark, not a cross-shot one: 180 prompts in seven categories, one generated video per sample at seed 42, scored with NexusScore (detector-cropped subject versus reference), NaturalScore (GPT-based naturalness), GmeScore (text relevance), FaceSim, aesthetics and motion. In the human domain Hailuo has the top total (60.20%; FaceSim 57.79, NaturalScore 74.52) while VideoMaker has the highest FaceSim (76.45) but a NaturalScore of 47.08, and the authors say ConsisID and Concat-ID have relatively strong FaceSim yet significant copy-paste issues. EntityBench characterises OpenS2V as evaluating single-subject preservation within individual shots. Code is Apache-2.0; the dataset and leaderboard are on Hugging Face.","status":"active","body":{"text":"OpenS2V-Nexus (arXiv 2505.20292, v3 read) is a single-clip subject-to-video benchmark, not a cross-shot one: 180 prompts in seven categories, one generated video per sample at seed 42, scored with NexusScore (detector-cropped subject versus reference), NaturalScore (GPT-based naturalness), GmeScore (text relevance), FaceSim, aesthetics and motion. In the human domain Hailuo has the top total (60.20%; FaceSim 57.79, NaturalScore 74.52) while VideoMaker has the highest FaceSim (76.45) but a NaturalScore of 47.08, and the authors say ConsisID and Concat-ID have relatively strong FaceSim yet significant copy-paste issues. EntityBench characterises OpenS2V as evaluating single-subject preservation within individual shots. Code is Apache-2.0; the dataset and leaderboard are on Hugging Face.\n\nCurator qualifications: All excerpts located; OpenS2V is single-clip, not cross-shot (scope note).\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/html/2505.20292v3","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2605.15199v1","note":"candidate evidence excerpts"},{"url":"https://github.com/PKU-YuanGroup/OpenS2V-Nexus","note":"candidate evidence excerpts"}]},"categories":["T06","T20"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/html/2505.20292v3","claim":"OpenS2V-Nexus (arXiv 2505.20292, v3 read) is a single-clip subject-to-video benchmark, not a cross-shot one: 180 prompts in seven categories, one generated video per sample at seed 42, scored with NexusScore (detector-cropped subject versus reference), NaturalScore (GPT-based naturalness), GmeScore (text relevance), FaceSim, aesthetics and motion. In the human domain Hailuo has the top total (60.20%; FaceSim 57.79, NaturalScore 74.52) while VideoMaker has the highest FaceSim (76.45) but a NaturalScore of 47.08, and the authors say ConsisID and Concat-ID have relatively strong FaceSim yet significant copy-paste issues. EntityBench characterises OpenS2V as evaluating single-subject preservation within individual shots. 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In the human domain Hailuo has the top total (60.20%; FaceSim 57.79, NaturalScore 74.52) while VideoMaker has the highest FaceSim (76.45) but a NaturalScore of 47.08, and the authors say ConsisID and Concat-ID have relatively strong FaceSim yet significant copy-paste issues. EntityBench characterises OpenS2V as evaluating single-subject preservation within individual shots. Code is Apache-2.0; the dataset and leaderboard are on Hugging Face.\n\nCurator qualifications: All excerpts located; OpenS2V is single-clip, not cross-shot (scope note).\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/html/2505.20292v3","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/html/2605.15199v1","note":"candidate evidence excerpts"},{"url":"https://github.com/PKU-YuanGroup/OpenS2V-Nexus","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"What does OpenS2V-Eval measure about keeping a given subject's identity in generated video, and what scores did closed and open subject-to-video systems get?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"OpenS2V-Nexus (arXiv 2505.20292, v3 read) is a single-clip subject-to-video benchmark, not a cross-shot one: 180 prompts in seven categories, one generated video per sample at seed 42, scored with NexusScore (detector-cropped subject versus reference), NaturalScore (GPT-based naturalness), GmeScore (text relevance), FaceSim, aesthetics and motion. 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